Gender Representations in Original and Translated Audiovisual Products for Children in Turkish: A Corpus- based Study i

Gender Representations in Original and Translated Audiovisual Products for Children in Turkish: A Corpus- based Study i | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Gender Representations in Original and Translated Audiovisual Products for Children in Turkish: A Corpus- based Study i Fatma Yücel Dinç, Sinem Sancaktaroğlu Bozkurt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7573349/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study focuses on gender representations of the cartoons at the intersection of audiovisual translation and translating for children. In parallel with the expansion of audiovisual translation, the growing diversity and accessibility of gender representations have brought into the question of what is being presented to children. Both original and translated audiovisual products teach certain gender roles to the child receiver, who is exposed to the pervasive nature of media consumption. Adopting the perspective of a corpus-based study in translation studies, this study investigates gender representations in original and translated cartoons on two widely viewed and preferred digital TV channels broadcasting in Türkiye, namely TRT Çocuk and Disney Channel Türkiye. With a target child receiver age range of 3–11, thirty cartoons aired on these digital TV channels between 2015 and 2021 were selected as the dual corpus of this study. The basic surmise is that children of the studied age are subjected to both types of products, taking in gender identity roles from two sources, produced in multiple different cultures. So, the aim is to identify what kind of gender roles children are exposed to through audiovisual products and whether there are any similar or conflicting images and messages. The method proposed by Abigail Walsh & Campbell Leaper (2020) and the activity categorization of Andrée Michel ( 1986 ) have been employed to identify gender representations on the original cartoons on the TRT Çocuk channel and the translated foreign cartoons of Disney Channel Türkiye. The analysis yielded relative rates on the basis of the following factors: the number of characters, talkativeness, speech behavior (support or suggestion), aggressive behavior, activities, and appearance. In conclusion, this study reveals that both the original and translated audiovisual products perpetuate numerous gender stereotypes, with limited variation in character number, appearance, or talking time, but notable differences in speech behavior and activity patterns. Institutional and editorial gatekeeping in translation choices reinforces existing gender schemas rather than broadening children’s exposure to diverse gender roles. Humanities/Cultural and media studies Social science/Cultural and media studies Biological sciences/Psychology Social science/Psychology gender representation corpus-based translation studies audiovisual translation translating for children cartoons content analysis 1 Introduction Children closely observe and internalize gender roles modeled in their families and social environments as they shape their identities through novel experiences. The original and translated content that children engage with significantly influence their understanding of gender. In many parts of the developing world, audiovisual products and their translations have become as influential, if not more so, than written texts. To this end, this study is conducted at the intersection of audiovisual translation, translation for children, and gender studies. The need for audiovisual translation practices has increased in recent years due to the rapid progress of technology and the shift from conventional media consumption habits to digital platforms. Children’s audiovisual content represents a crucial domain of audiovisual translation, combining elements of translating for children with considerations of media accessibility (O’Hagan, 2012; Pérez-González, 2014 ) and cultural adaptation (Klingberg, 1986 ; Oittinen, 2000 ) among many other fields of study. In addition, scholars such as Oittinen ( 2000 ) and O’Sullivan (2005) have explored the unique challenges posed by translating children’s texts, emphasizing the interplay between verbal and visual narratives. Since children's television shows have a global audience and a significant impact on young people, their adaptation has also drawn the attention of scholars (Moran, 2006 ; Schober & Olson, 2018 ). In particular, gender representation within these translations has emerged as an essential aspect of research, with scholars such as Di Giovanni ( 2010 ) and Josephy-Hernández ( 2019 ) examining how translation choices can reinforce or challenge gender norms. As the nexus of translation for children, gender studies and audiovisual translation, this study aims to unveil gender representation that children aged 3–11 years in Türkiye were exposed to between 2015 and 2021 by comparing the original and translated corpus from two widely viewed digital TV channels, namely TRT Çocuk channel and the Disney Channel Türkiye. Additionally, it seeks to shed light on how female and male genders are portrayed for a specific age range in both translated and original audiovisual products. Determining whether children are exposed to similar and contrasting gender-related images and messages—and examining their nature, frequency, and potential impact—is another goal of the study. Another focus of the study is to reveal the similarities and differences between the translated and original audiovisual corpus. A total of one hundred and thirty-four examples from the dual corpus was analyzed to identify gender stereotypes and speeches. First of all, the original cartoons of TRT Çocuk was coded with the content analysis method suggested by Walsh and Leaper ( 2020 ). Secondly, the translated corpus was coded in the same way. During the coding procedure of the translated corpus, the English versions were compared with the Turkish dubbings whether there was a translation shift in terms of gender representation. The content analysis method suggested by Walsh and Leaper ( 2020 ) in “Content Analysis of Gender Representations in Preschool Children’s Television” was used for the coding procedure; the number of characters, talkativeness, speech behaviors, aggressive behaviors, activities, and appearances were examined in the thirty cartoons, composed of both original and translated audiovisual products. Moreover, the categorization suggested by Michel ( 1986 ), in his “Eliminating Sexism from Children’s Literature and School Textbooks” was also used to expand specific operational definitions for coding gender-stereotyped activities. 2 Theoretical Considerations The intersection of gender, translating for children and audiovisual translation brings about an interdisciplinary approach that focuses on the translated audiovisual products for children from gender perspective. These disciplines were selected for their relevance to compare original and translated cartoons in Turkish, offering a lens through which to reveal the similarities and disparities in gender representation. The discussion proceeds by first addressing translating audiovisual products for children, then extending to the translating gender-related issues in such content. 2.1 Translating Audiovisual Products for Children While children’s literature was long overlooked in academia, recent decades have witnessed a growing body of research dedicated to both children’s literature and its translation. Worldwide, translations make up a considerable amount of the literature that children read and (or) listen. Gillian Lathey ( 2006 , p. 2) claims that translated works created for children promote global peace by helping children comprehend different cultures and nations. Thus, the translation of children’s literature has recently become a rich area of scholarly investigation, addressed in different peer-reviewed journals such as Bookbird: A Journal of International Children’s Literature and Children’s Literature in Education , as well as master and PhD dissertations (e.g., Apap, 2014 ; Zoli, 2023 ), among many other academic works. Foundational studies by scholars such as O’Sullivan (2000) and Oittinen ( 2000 ) have also deepened our understanding of the interplay between text, image, and ideology in translated children's books. These studies also highlight the difficult choices translators make when adapting texts for young audiences. In the Turkish context, valuable contributions have also been made on the historical development of children’s literature (Neydim, 2000 , 2006 , 2020 ), characteristics and challenges of children’s literature and translation (Erten, 1993 , 2011 ), translating culture in children’s literature (Hastürkoğlu, 2020 ; Kansu-Yetkiner 2010 ; 2014 ), translating sound in children’s literature (Dinçkan, 2017 ) alongside many other notable studies in the field. Furthermore, the increase in television programs, game shows, cartoons, animations, puppet shows, films and videos aimed at children, in addition to books, has made it possible to examine child-oriented translation products under the subfield of translation studies, namely audiovisual translation. Consequently, core issues traditionally associated with children's literature such as dual address problem (O’Sullivan, 1993 ), relationship between written and visual materials (Oittinen, et al., 2017 ), didactic concerns (Van Collie & Walter, 2014) can now also be extended to the realm of translating for children in audiovisual context. In other words, the characteristics of audiovisual products translated for children exhibit strong parallelism with those of the research categorized for the translation of children's literature. Thus, the abovementioned traits—cultural context adaptation (Koigeldiyeva, 2023 ), ideological manipulation (Lorenzo, et al., 2016 ), features of orality (Varga, 2012 ), text and image (Masero, 2016 ), the dual addressee and such—can be observed in studies at the intersection between audiovisual translation and translating for children. Children's programs, which have the abovementioned characteristics, include many television shows, such as puppet shows, advertisements, game shows, cartoons, animations, films and videos. As television culture in the USA developed in the 1950s, children's audiences became a part of an entertainment-focused society (Schober & Olson, 2018 ). In 1969, for example, Joan Ganz Cooney and Lloyd Morrisett created the educational children's television series Sesame Street , which was produced by Sesame Workshop and teaches American preschoolers about the alphabet, numbers, health and safety, and other subjects that are pertinent to children's everyday lives. In addition, children from South Africa, India, and Indonesia around the world have the opportunity to learn from and enjoy distinctive Muppet characters, which are occasionally widely shared and other times extensively localized (Calbreath-Frasieur, 2016 , p.99). According to Moran ( 2006 ), "children living in Europe, Asia, Latin America, truly almost anywhere in the world, have the opportunity to watch an episode of Rugrats, Power Rangers, or a version of Sesame Street from their living room or broadcast in the original English, dubbed into their native language" (p. 288). As Okyayuz ( 2017 ) stated, the Muppet Show , one of the first audiovisual products presented to child receivers in Türkiye, was adapted into Turkish sense of humour with the contributions of the translators (p. 138). These worldwide circulating audiovisual materials illustrate not only the cultural adaptability of children's programs but also the growing demand for localized, linguistically accessible content. This widespread international distribution is often facilitated through translation, which becomes a practical necessity rather than a creative luxury. The exorbitant cost of creating high-quality animations for a child audience may be why many nations require and favor translated audiovisual products. When it comes to translating children's programs, there can be several reasons why dubbing is more commonly used than subtitling. Dubbing offers accessibility for young audiences (Borodo & Díaz-Cintas, 2025 ), it creates the impression of a domestic product, and remains suitable for both children and adults who are not yet literate. However, despite these advantages, dubbing also presents certain challenges. It is important to anticipate the unforeseen issues that may arise from translators’ decisions since the target child receiver brings with them pedagogical sensitivity. If translation decisions are not the target child receiver oriented, all decision makers such as producers, editors, audiovisual production specialists and translators may encounter ethical and functional dilemmas. In this context, cultural adaptation becomes vital. 2.2 Translating Gender-related Issues in Audiovisual Products for Children Given that this study focuses on the gender representation of the original and translated children’s programs, it is necessary to define gender representation. In its broadest sense, gender representation concerns how men and women—and, in this study, girls and boys—are portrayed. In society, gender classifications are established through stereotypes based on expected values, traits, and behaviors (Lott & Maluso, 1993). Michel ( 1986 ) stated that "stereotypes can refer to the physical characteristics of a group (e.g., women or men), the intellectual, emotional, or volitional qualities of its members, or certain aspects of its position in society" (p. 16). For example, the statement "men are aggressive" is sexist since it suggests that males are naturally violent. The significance of early childhood education is overlooked in this statement, as it encourages boys to channel their aggression onto others while encouraging girls to suppress this aggressive emotion or conduct (Michel, 1986 , p. 16). Özdoğan observes (2025) that "gender, as a fundamental aspect of human existence, has implications on how individuals perceive themselves and their roles in society (p.30)". These perceptions are not innate but socially constructed, and they are permanently reinforced through cultural products consumed in early childhood. Representations of gendered behavior appear not only in written materials such as picture books, school books, children’s magazines and story books, but also in audiovisual products such as children's television programs. In both cases, these materials contribute the internalization of gender norms, demonstrating the need to scrutinize how translation practices may either perpetuate or challenge such representations. Thus, in the realm of translating children's literature through a gender lens, numerous studies, research and theses have been conducted around the world and in Türkiye on different subjects such as translating gender-related content (Chen & Song, 2025 ), gender roles in children’s books (Çelik, 2020 ), feminist translating strategies used in marginal fairy tales (Dağabak, 2018 ), translated feminist children’s literature (Demirhan, 2017 , 2020 , 2022 ) and gender representation in picture books (Gooden & Gooden, 2001) among many others. Given that gender-focused themes in children's literature can shape young readers' understanding of gender roles, translation plays a critical role in either challenging or reinforcing existing stereotypes. According to Taguchi ( 2007 ), "Poststructural thinking focuses children’s own active ways of acquiring what they regard as suitable gender positions for themselves, which means that different gender positions could be desirable for children depending on powerful discourses in their context" (275). Therefore, gender representations play a significant role in the books and cartoons children are exposed to or presented with. In other words, translated versions of written or audiovisual materials, as well as their original versions, are among the factors that shape children's perceptions of gender. In her study, Demirhan ( 2022 ) underlines that "source texts are not gender-friendly all the time. When translating patriarchal and androcentric children’s literature, by revising and adapting the source texts from a gender positive lens, translators’ interventions in gendered and anti-feminist ideas has become apparent” (154). In other words, by intervening against sexist and anti-feminist notions, it is possible that ii translators can make dubbed and subtitled materials gender-friendly in contrast to their originals while translators adapt these works to the needs of children and the culture, they live in. While much of the foundational research on gender representation and translation has centered on children's literature, recent scholars have started to extend these concerns to audiovisual translation, which is also a highly influential medium in shaping children's gender perceptions such as dubbing feminist discourse (Bazzi, 2024 ), shifts in audiovisual translation for children (Di Giovanni, 2010 ), gender translation of an anime film (Josephy-Hernández, 2019 ), and gender stereotypes in animated cartoons (Tomoiagă, 2017 ) among many others. A common point across these studies is the depiction of children's gendered bodies and behaviors, as well as their talks in the source language and the translations into the target languages. Children's perception of gender identity is shaped by the patterns in the audiovisual materials they consume. Therefore, the young audience's cognitive perception is influenced by the audiovisual product's narrative and language, whether or not it is sexist, and how egalitarian the discourses in gender roles like mother-father and sister-brother are. This is why, as explored in this study, it is essential to examine how audiovisual products that teach children gender lessons are conveyed through translation. 3 Methodology This study engages in a content analysis of a total of one hundred and thirty-four examples from two main television channels, TRT Çocuk and Disney Channel Türkiye. The content analysis method suggested by Walsh and Leaper ( 2020 ) and the activity categorization of Michel ( 1986 ) were employed for the coding procedure as previously mentioned in introduction. Each process is explained in detail below. 3.1 Limitation The study began with the selection of two widely viewed and preferred children's channels, one of which aired mostly domestic Turkish content (TRT Çocuk) and the other of which mostly aired foreign content (Disney Channel Türkiye). The channel selection process can be briefly explained as follows in the Table 1: Table 1 Viewing the measurements of the children’s TV channels provided by TİAK [the TV Audience Research Company]. In terms of viewing rates of Audience Research Company [ TİAK - Televizyon İzleme Araştırma Anonim Şirketi ] which is a joint stock corporation, TRT Çocuk is ranked highest, followed by Cartoon Network, as shown in Table 1. Using the years 2015–2021 as the basis, TRT Çocuk and Disney Channel Türkiye were selected for their wide recognition, diverse content, and, most importantly, their steady position within the top four. It is also necessary to provide a brief overview of TRT Çocuk and Disney Channel Türkiye. TRT Çocuk, affiliated with the Turkish Radio and Television Corporation (TRT), is an official channel and the first local children’s channel in Türkiye, aired on November 1, 2008. It highlights that it provides learning-focused and educational content for preschoolers and school-age children (aged 3–11) and strives to foster the target audience's intellectual growth. Most of the cartoons and children's programs that air on TRT Çocuk are produced domestically, with very few translated products, according to an analysis of the network's weekly broadcast flow (for further information, see https://www.trtcocuk.net.tr/ and https://www.trtcocuk.net.tr/yayin-akisi/ ). The Disney Channel, an American basic cable and satellite television network, was established in April 1983. The Disney Channel Worldwide, owned by Walt Disney Television, is a global entertainment portfolio with 118 child-driven, family-friendly channels and/or channel feeds accessible in 34 languages and more than 164 countries. With respect to the introduction of Disney audiovisual products in Türkiye, the first Disney Channel was launched in 2007 by Digiturk, a subscription-based Turkish satellite service. On January 12, 2012, it began broadcasting as a free-to-air channel via the Türksat 2A satellite. Ten years later, the Disney Channel Türkiye announced on its Instagram page that it would shut down. The broadcast was stopped on March 31, 2022. The majority of Disney-made content is currently accessible in Türkiye through Disney+. For this study, all cartoons were watched and analyzed through TRT Çocuk YouTube and Disney Channel Türkiye on YouTube. After deciding on the channels to be examined for the study, 30 television series aired on these channels were selected, comprising 21 from TRT Çocuk and 9 from Disney Channel Türkiye. The selected cartoons are decided based on age range of the viewers and distribution of female and male characters as well as the distribution of human and non-human characters. Below (in Table 2 and Table 3) you can see the details of the selected original cartoons aired on TRT Çocuk. Table 2 Original TV series for the age range between 3 and 6 (aired on TRT Çocuk) Table 3 Original TV series for the age range between 6 and 11 (aired on TRT Çocuk) The Turkish-made TRT Çocuk cartoons listed above on the Tables 2 and 3 include Akıllı Tavşan Momo [The Smart Rabbit Momo], Aslan [Aslan], Ciciki [Ciciki], Doru [Doru], Ege ile Gaga [Ege and Gaga], Elif ve Arkadaşları [Elif and Her Friends], Elif’in Düşleri [Elif’s Dreams], Erdem [Erdem], Kaptan Pengu ve Arkadaşları [Captain Pengu and Friends], Kare [Square], Kuzucuk [Kuzucuk], Niloya [Niloya], Pırıl [Pırıl], Yade Yade [Yade Yade], Bulmaca Kulesi [Puzzle Tower], Emiray [Emiray], İbi [İbi], Maysa ve Bulut [Maysa and Bulut], Nasreddin Hoca Zaman Yolcusu [Nasreddin Hodja, Time Traveler], Rafadan Tayfa [Rafadan Tayfa] and Z Takımı [Team Z ]. These cartoons generally focus on the adventures of the main characters (e.g., The Smart Rabbit Momo) , school and family dynamics (e.g., Elif and Her Friends ), promoting environmental awareness (e.g., Captain Pengu and Friends ), and conveying traditional and moral lessons to children (e.g., Maysa and Bulut, Ciciki ). As for the selected translations aired on Disney Chanel, same parameters were valid. Below (in Table 4) you can see the details of the selected translated cartoons aired on Disney Channel. Table 4 Translated television series for the age range between 3 and 11 years (Disney Channel Türkiye) Translations listed above (in Table 4) include Phineas and Ferb [Fineas ve Förb], Sofia the First [Prenses Elena], Gravity Falls [Esrarengiz Kasaba], Star vs. the Forces of Evil [Star Kötü Güçlere Karşı], PJ Masks [Pijamaskeliler], Puppy Dog Pals [Bingo ve Roli’nin Maceraları], Vampirina [Vampirina], Bluey [Bluey] and Big City Greens [Greenlerin Büyükşehir Maceraları]. Many of these cartoons, similar to TRT Çocuk's themes, center on the adventures of the main characters. They also appeal to older children by exploring themes such as invention and construction (e.g., Fineas and Ferb ), journeys through fantastical worlds (e.g., Star vs. the Forces of Evil ), and superperson transformations (e.g., PJ Masks ). Having introduced the selected television series and outlined their central themes, the next step involves examining how gender representations were revealed within these cartoons. To achieve this, a systematic coding and content analysis was employed to uncover the underlying gender stereotypes, and the results were discussed statistically. In this framework, 3 episodes from each selected cartoon broadcast on TRT Çocuk and Disney Channel Türkiye were analyzed, amounting to a total of 134 examples. applying a coding procedure to the dual corpus is essential for uncovering gender stereotypes. First, original corpus was coded; second, the translated corpus was coded; third, the source version was compared with the translated version to identify potential translation shifts. Finally, the examples were classified and analyzed according to the operational definitions established for the coding procedure. 3.2 Coding and Content Analysis The content analysis method suggested by Walsh and Leaper ( 2020 ) in “Content Analysis of Gender Representations in Preschool Children’s Television” and the activity categorization of Michel ( 1986 ) as suggested in “Eliminating Sexism from Children’s Literature and School Textbooks” were used for the coding procedure of this study. While the coding procedures such as the number of characters, talkativeness, speech behaviors (suggestion and support), aggressive behaviors and appearances are followed as in Walsh and Leaper’s study (2020), the activity categorization, one of the six coding procedures, was expanded following Michel’s ( 1986 ) study. Unlike Walsh and Leaper’s ( 2020 ) work, some activity definitions were used on the basis of Michel’s categorization since watched episodes and transcribed cartoons largely overlapped Michel's (1986) activity schema. As you will see the coding procedures in detail below, the methods of Walsh & Leaper ( 2020 ) and Michel ( 1986 ) were combined for this study. Each coded variable of the dual corpus was assessed from the perspective of gender representation. First, the operational definitions for the coding procedure were clearly defined. Second, the randomly selected three episodes for each cartoon in the dual corpus were watched and transcribed considering the operational definitions. Third, the presence of each behavior was recorded on a spreadsheet. Fourth, a sum of each variable for each gender was coded to determine the frequency of each behavior in the original and translated texts. Moreover, the source texts and dubbed versions broadcast on Disney Channel Türkiye were compared to identify gender-based speeches and gender representations interlingually. It was essential to compare the English and Turkish transcriptions of Disney-made audiovisual products during coding since it was crucial to examine whether there was a translation shift regarding gender representation while analyzing speeches on Disney Channel Türkiye, where audiovisual products undergo dubbing translation. Finally, each coding was statistically determined and interpreted separately and comparatively. The operational definitions for the coding procedure are detailed as follows: The number of characters in each episode of the cartoon coded the total number of female characters and male characters in a speaking role or a name. The characters' gender was determined by their appearance, pronouns, and names. The number of human and nonhuman characters was counted independently for each sex. Talkativeness For this study, the talking time for each gender reflected the amount of time spent by any female or male character during time-sampled intervals. The durations during which male and female characters simultaneously sang and yelled out were excluded. Speech behavior during each episode was thoroughly watched for coding purposes, and relevant dialogues were transcribed. The operational definitions in which the dual corpus is scanned are as follows: suggestions include proposals for actions not in the form of commands, whereas supportive speeches include consoling, emotional advice, and encouragement on the basis of Walsh and Leaper’s ( 2020 ) content analysis. Praise was also coded as supportive speech, reflecting their presence in the sampled television series. Aggressive behavior was coded to determine whether female or male characters were more aggressive in the original and translated cartoons. According to Walsh and Leaper's (2020) content analysis, aggressive behavior includes any direct or indirect acts that cause harm to others, such as insulting, hitting, social exclusion and harmful gossip. For this study, aggression is classified as verbal or nonverbal rather than direct or indirect. Aggressive behavior was coded as one variable since aggression was considered a low-occurring behavior. Activities involve feminine-stereotyped activities (performance arts such as singing, dancing, and visual arts) and masculine-stereotyped activities (physical activities and constructive activities) in Walsh and Leaper’s ( 2020 ) study. However, in Michel’s ( 1986 , p. 51) study, the types of activities were divided into five categories: domestic tasks (e.g., cooking, food preparation), contributing to the education/well-being of children (emotional and physical support), professional activities (roles of doing work and giving orders), leisure activities (sport, art, hobbies, artistic activities, games, adventure) and political or social activities (mayor of a town, head of a club or social, artistic, cultural or sporting association). Playing with children (pretend-play) and being a role model were expanded to the category of contributing to the well-being of children, in addition to the definitions of Michel ( 1986 ). Appearance Three components of the outlook—body type, accessories, and color—were used to code appearance in this research. Only human characters were coded for their body types. All human and nonhuman characters’ clothes were coded for colors and accessories once for each outfit in each episode of the sampled television series. After this content analysis, the gender patterns of the female and male characters were identified by comparing the original corpus of TRT Çocuk and the translated corpus of Disney Channel Türkiye. Below, the findings will be statistically discussed. 4 Findings and Discussion Below, findings will be presented under certain subheadings such as overall data on the number of characters, overall data on taklativeness, overall data on speech behavior, overall data on aggressive behavior, overall data on activities and overall data on appearance. Overall data on the number of characters Approximately twice as many male characters as female characters were found in the original corpus aired on TRT Çocuk. While the main characters were equal in number of female and male main characters for determining the data, the analysis focused on the number of characters with consistent presence across the three episodes, identifying all characters who had at least one speaking role. For instance, in the original cartoon İbi , which features a female protagonist, viewing three episodes of approximately 11 minutes each revealed that 6 female and 12 male characters—both human and non-human—had at least one speaking role. Similarly, cartoons such as Emiray [ Emiray ], Nasreddin Hoca Zaman Yolcusu [Nasreddin Hodja, Time Traveler], and Rafadan Tayfa [ Rafadan Tayfa ] feature male protagonists, while others like Z Takımı [ Team Z ], Maysa ve Bulut [Maysa and Bulut], and Bulmaca Kulesi [ Puzzle Tower ] include only one female character alongside at least two male characters. As a result, although the number of female and male protagonists was equal, the overall character distribution reveals a clear predominance of male figures. When examining the translated corpus (see Table 4), it was found that among 9 selected cartoons, 4 feature female protagonists ( Sofia the First, Bluey, Vampirina and Star vs. the Forces of Evil) , 3 feature male protagonists ( Fineas and Ferb, PJ Masks and Puppy Dog Pals ), and 2 have equally both female and male protagonists ( Big City Greens and Gravity Falls ). For example, in Sofia the First , which centers on a female main character, analysis of episodes totaling 24 minutes revealed that 8 female and 9 male characters —both human and non-human—had at least one speaking role. This demonstrates that the presence of a female protagonist does not mean that it presents predominantly female representation. Similar result to the original corpus, the translated corpus also shows a higher number of male than female characters (Yücel Dinç, 2024 ). Overall Data on Talkativeness (Talking Time) Talking time is important for portraying gender representation in cartoons. As shown in Table 5 , talking time of female and male characters in the original and translated TV series is presented. Table 5 Talking time of female and male characters in the original and translated television series N Mean Std. Dev. T Test Sig (p) Disney Channel Türkiye Female 227 7,1806 10,9693 -0,528 0,598 Male 236 7,7288 11,3435 TRT Çocuk Female 513 5,1442 7,4597 -7,946 0,000 Male 603 12,2454 19,0350 When the talking time of female and male characters was analyzed, a sex difference was observed for TRT Çocuk (p = 0.000 < 0.05). The data indicate that male characters have an average talking time of 12.2454 ± 19.0350 seconds, whereas female characters have an average talking time of 5.1442 ± 7.4597 seconds. It can be concluded that the talking time of male characters was greater than that of female characters, and this difference was statistically significant. For Disney Channel Türkiye, no significant difference between genders was found (p = 0.598 > 0.05). The talking times of the male and female characters were equal on this channel. Examining the relationships between gender, channel and age group in terms of time is also important. Below you can see the Table 6 . Table 6 The relationships between gender, channel and age group in terms of time N Mean Std. Dev. T Test Sig (p) TALKING TIME Gender Female 740 5,7689 8,73125 -7,385 0,000 Male 839 10,9750 17,33506 Channel TRT Çocuk 1120 9,0348 15,35041 2,000 0,046 Disney Channel Türkiye 463 7,4600 11,15291 Age 3–6 Age 1159 8,8412 15,13682 1,231 0,218 6–11 Age 424 7,8443 11,53899 When the characters’ talking times were examined, gender differences were found between the groups (p = 0.000 < 0.05). The data revealed that male characters had an average talking time of 10.975 ± 8.731 seconds, whereas female characters had an average talking time of 5.7689 ± 17.335 seconds. In other words, male characters’ talking time was almost twice as long as female characters’ talking time was, and this difference was statistically significant. Channel-specific analysis revealed that the average talking time on TRT Çocuk Channel was 9.0348 ± 15.350 seconds, whereas the average talking time on Disney Channel Türkiye was 7.4600 ± 11.153 seconds. This indicates that characters on the TRT Çocuk generally talked for longer, and this difference was statistically significant. These quantitative findings are consistent with a broader discussion of gender stereotypes in audiovisual products for children. The portrayal of boys talking more than girls in cartoons, whether in original or translated versions, may lead young viewers to internalize the idea that conversational dominance is a natural male characteristic. Leaper ( 2014 ) claims that variations in talking time may reflect speakers’ dominance through their control of conversation. From this perspective, a child receiver who models gender roles from on-screen portrayals may come to perceive it as normal for boys to talk more and dominate conversations. Overall Data on Speech Behavior Speech behavior is presented in two separate tables, categorized as supportive speech or suggestion. In table 7, the statistical results of the distribution of supportive speech by female and male characters are shown. Table 7 Comparing the use of supportive speech in TRT Çocuk and Disney Channel Türkiye according to gender Supportive Speech-Female Value Label N Mean Std. Dev. F Sig. channel 63 episodes TRT Çocuk 14 0,2258 0,08925 Model 13,304 ,001 27 episodes Disney Channel TR 10 0,4167 0,15386 Stable 59,295 ,000 Supportive Speech-Male Value Label N Mean Std. Dev. F Sig. channel 63 episodes TRT Çocuk 13 0,2097 0,10141 Model 8,696 0,006 27 episodes Disney Channel TR 11 0,4583 0,15801 Stable 63,030 0,000 Considering the use of supportive speech in the original cartoons in TRT Çocuk, although no clear difference in female characters’ (0,2258) use of supportive speech and male characters’ (0,2097) use of supportive speech is apparent at first glance, the numerical difference is significant. Given that female characters are represented by supportive speeches within the framework of traditional gender norms, this finding suggests that TRT Çocuk content aligns with traditional portrayals of femininity, which often emphasize encouragement, cooperation, and emotional support. This is consistent with von Flotow’s ( 2009 ) idea that gender-focused work highlights how language reflects power relations between the sexes and how women’s roles are often socially constructed through language practices (p.122). For example, in the original cartoon Aslan [Aslan], a female character is depicted praising a male character’s idea, while in Akıllı Tavşan Momo [The Smart Rabbit Momo] , a female character takes on a nurturing role by comforting her friend (for more examples, see Yücel Dinç, 2024 ). Such patterns may subtly reinforce to child audiences that women are expected to validate and nurture others, particularly male counterparts. When examining supportive speeches in Disney Channel Türkiye as the translated corpus, a different pattern emerges: no significant difference was observed between female characters’ (0,4167) and male characters’ (0,4583) use of supportive speech. This relative balance offers that the translated content may depart from the conventional gendered speech stereotypes seen in the original TRT Çocuk cartoons. For instance, in Fineas and Ferb , both female and male characters’ speeches can be equally supportive and have been translated faithfully: In the ‘Rollercoaster’ episode, the speech of a female character, "It really was the coolest coaster ever. You guys make a great team" is rendered as "Bu yaptığın harikaydı, Fineas. Bindiğim en muhteşem hız treniydi." Likewise, in the ‘Tourist Trapped’ episode of Gravity Falls , a supportive speech of a male character, "Don’t worry Mabel, I will save you from the zombie!" is translated faithfully as "Merak etme Mabel, seni o zombilerden kurtaracağım!" Instead of reinforcing a clear-cut association between supportive speech and femininity, the translations present supportive language as a more gender-neutral way. While this does not necessarily indicate a complete break from traditional representations, it may expose child receivers to a slightly more egalitarian model of communicative behavior, in which encouragement and consolidation are not framed as predominantly female traits. Suggestion is another speech behavior. In Table 8 , you can see the statistical results showing the distribution of suggestion speech behavior by female and male characters. Table 8 Comparing the use of suggestions in TRT Çocuk and Disney Channel Türkiye according to gender Suggestion-Female Value Label N Mean Std. Dev. F Sig. Channel 63 episodes TRT Çocuk 39 0,6290 0,14051 Model 32,081 0,000 27 episodes Disney Channel TR 10 0,4167 0,15386 Stable 105,194 0,000 Suggestion-Male Value Label N Mean Std. Dev. F Sig. Channel 63 episodes TRT Çocuk 115 1,8548 0,36629 Model 42,988 0,000 27 episodes Disney Channel TR 11 0,4833 0,11801 Stable 103,076 0,000 As Walsh and Leaper ( 2020 ) noted, previous studies have shown that boys are more likely to offer suggestions than girls (p. 23). This pattern is clearly reflected in TRT Çocuk (original corpus), where male characters present significantly more suggestion behavior (1,8548) than female characters (0,6290). In contrast, Disney Channel Türkiye showed no notable gender difference, with comparable scores for males (0,4833) and females (0,4167). Male-led shows such as Aslan [Aslan], E miray [ Emiray ], and Rafadan Tayfa [ Rafadan Tayfa ] on TRT Çocuk featured more male characters, who dominated speech and actions. For example, in Aslan , the male protagonist consistently leads and makes more suggestions, reinforcing male-centered narratives in the original corpus (for more examples, see Yücel Dinç, 2024 ). These findings suggest that the original TRT Çocuk content often associates suggestion-making, a form of verbal initiative closely linked to decision-making and authority, with male characters. When the translated corpus is examined in comparison with the source text, it presents statistically almost equally gender representation. For example, in the episode ‘Catboy’s Cloudy Crisis’ of PJ Masks , it is observed that suggestions from both genders were translated faithfully to the source text: The male character’s speech "We need to find out where these clouds are coming from" is rendered literally as "Bulutların nereden geldiğini öğrenmemiz gerekiyor", while the female character’s suggestion "Let’s follow the hose" is likewise faithfully dubbed as "Hortumu takip edelim." In terms of the statistical finding of suggestion overall, Disney Channel Türkiye (translated corpus) potentially offers a more egalitarian distribution of verbal leadership. This divergence could be the result of different source material norms, translation practices that downplay gendered speech patterns, or the influence of more globally oriented storytelling in Disney productions. For young audiences, such differences in representation matter: while TRT Çocuk may implicitly signal that leadership and initiative are male domains, Disney Channel Türkiye’s portrayals could normalize the idea that both boys and girls can take active roles in guiding interaction and decision-making. The table below presents the statistical differences in aggressive behavior showing the distribution by female and male characters. Overall Data on Aggressive Behavior Aggressive behavior is presented in Table 9 , with the statistical results showing the distribution of aggression by female and male characters. Table 9 Differences in aggressive behavior according to gender Aggressive behavior - Female Value Label N Mean Std. Deviation T Test Sig (p) channel 63 episodes TRT Çocuk 0 a . . - - 27 episodes Disney Channel TR 24 ,4167 ,65386 Aggressive behavior - Male Value Label N Mean Std. Deviation T Test Sig (p) channel 63 episodes TRT Çocuk 1 1,0000 . 0,807 0,428 27 episodes Disney Channel TR 24 ,4583 ,65801 All p values were greater than 0.05 when the levels of male and female aggression for TRT Çocuk and Disney Channel Türkiye were compared, indicating no statistically significant difference between the two variables. In previous studies, it was observed that aggression was more prevalent in male representations (Ercen et al., 2022 ) and the frequency counts in the present study show a similar tendency. In the original corpus, only one instance of aggressive behavior was observed, and it was represented by a male character In TRT Çocuk. This instance involves a threatening statement by a male character in the Emiray episode titled Buğdayları Kim Yere Döktü? [Who Spilled the Wheat on the Ground?]. When comparing aggression between female and male characters in the translated corpus, a far greater number of depictions of aggression by male characters than female characters were observed. Aggressive behaviors were predominantly found in cartoons aimed at older children, such as Big City Greens , Gravity Falls , and Star vs. The Forces of Evil . For instance, Big City Greens features a fictional rejection scene involving a female character, which may subtly reinforce gender-based power dynamics and influence children's perceptions of how girls are treated in social interactions. Although it depicts an imaginary scenario, this scene—where a male character sarcastically rejects a girl’s proposal and then kicks her—is still conveyed to the child audience through translation (for more examples, see Yücel Dinç, 2024 ). Such portrayals, even when fantastical, can implicitly model forms of male dominance and normalization of physical or verbal aggression toward female characters. This stark difference between original and translated suggests that the original may consciously avoid aggressive behavior, especially physical aggression, in their narratives, possibly due to local broadcasting standards, cultural expectations about age-appropriate content, or a greater emphasis on promoting prosocial interactions. Disney Channel Türkiye, on the other hand, particularly in programming for older children, may reflect both the nature of the source material and different cultural thresholds for acceptable depictions of interpersonal conflict. As a result, in the rare examples where aggression does occur, it is consistently represented by male characters, while female characters appear in more submissive roles. Across both the original and translated cartoons, a recurring pattern emerges: aggression is exclusively associated with male characters. Overall Data on Activities Since the data come from two different channels, the activities coded in the table are categorized as channel 1 and 2. The Table 10 examines the relationship between channels, age groups, and activities. Table 10 Similarities and differences in activities according to channel and age Chi-Squre Tests Sig (p) Eta Correlation Cor (Sig) Channel 1 Domestic Tasks 1 11,551 0,041 0,559 -0,310 0,062 Channel 2 Domestic Tasks 2 29,000 0,000 1,000 -0,948 0,000 Channel 1 Contributing Education 1 24,593 0,000 0,877 -0,724 0,000 Channel 2 Contributing Education 2 8,559 0,036 0,543 -0,400 0,032 Channel 1 Professional Activities 1 30,741 0,000 0,877 -0,732 0,000 Channel 2 Professional Activities 2 29,000 0,000 1,000 -0,909 0,000 Channel 1 Leisure Activities 1 51,954 0,001 0,946 -0,669 0,000 Channel 2 Leisure Activities 2 66,000 0,000 1,000 -0,923 0,000 3–6 years Domestic task 1 2,014 0,847 0,233 0,042 0,805 6–11 years Domestic task 2 11,343 0,124 0,625 -0,040 0,835 3–6 years Contributing Education 1 2,125 0,908 0,258 -0,066 0,721 6–11 years Contributing Education 2 6,337 0,096 0,467 0,007 0,972 3–6 years Professional activities 1 7,778 0,455 0,441 -0,215 0,182 6–11 years Professional activities 2 12,814 0,118 0,665 0,111 0,565 3–6 years Leisure Activities 1 21,955 0,523 0,615 0,023 0,864 6–11 years Leisure Activities 2 29,722 0,428 0,671 0,098 0,431 A comparison of domestic tasks, educational contributions, professional activities, and leisure activities across channels and age groups reveals significant correlations: According to the statistic findings, domestic task representation does not differ significantly between the 3–6 and 6–11 age groups, but domestic tasks (0.000) vary strongly by channel, with TRT Çocuk and Disney Channel Türkiye showing different patterns of how often domestic chores are assigned to characters. Female characters were more often shown performing domestic tasks, especially in the original TRT Çocuk corpus, while such representations were rare in the translated content of Disney Channel Türkiye. For instance, in local cartoons such as Akıllı Tavşan Momo [The Smart Rabbit Momo], Elif’in Düşleri [Elif’s Dreams] , Maysa ve Bulut [Maysa and Bulut] , and Niloya , female characters are most often portrayed cooking. A notable example appears in the Bayram [Festival] episode of Elif’in Düşleri [Elif’s Dreams] , where the mother invites her daughter to wrap grape leaves, a scene that implicitly frames cooking as a feminine domestic task. As the comparison of contributing children’s education especially being a role model by adults, differences come from channel, not age group. Correlations (up to − 0.724, p < .001) indicate that channels portray educational contribution very differently. Likely TRT Çocuk emphasizes more contributing education content, while Disney Channel Türkiye portrays less on this aspect. As the findings present, professional activity differences are due to channel, not age. Correlations (–0.732 to − 0.909, p < .001) again show a channel-based divergence: Occupational roles across both corpora also revealed persistent gender representations. In the original TRT Çocuk cartoons, positions such as newscasters, salespersons and some authoritative roles were displayed as masculine-stereotyped activities. Similarly, in the translated cartoons, male characters were more often depicted in traditionally masculine-stereotyped occupations, including weathermen, CEOs, mayors, and school principals. In contrast to these masculine-stereotypical activities, an analysis of the 63 episodes in the original corpus revealed that only one episode of the cartoon Z Takımı [ Team Z ], included a female newscaster. Likewise, out of the 27 episodes in Disney Channel Türkiye, only one episode of Big City Greeens had a female reporter. Although such representations are rare, the presence of these exceptions is not insignificant. The portrayals of women in professional or authoritative roles can act as deconstructing stereotypes, offering children alternative models of what women can do in both the public and professional spheres (Yücel Dinç, 2024 ). The findings present that no age-related differences, only channel-based. Considering the category of leisure time activities in the original corpus, male characters were nearly twice as represented in adventure and sports-related activities as female characters such as İbi, Nasreddin Hoca Zaman Yolcusu [Nasreddin Hodja, Time Traveler], Rafadan Tayfa [Rafadan Tayfa] and Z Takımı [Team Z ]. Similar representations are also evident in the translated corpus. As seen in Sofia the First , boys are actively involved in activities like the Flying Derby, while girls are passively portrayed as spectators. In the episode ‘Just One of the Princes’ of Sofia the First , Sofia expresses her desire to participate in the Flying Derby. Her stepsister Amber, however, attempts to dissuade her, remarking that "Flying Derby isn’t a princess thing." This exchange exemplifies how certain leisure activities, particularly sports, are framed as male domains and, through translation, reinforce gendered lessons for child audiences. Similar patterns appear in the cartoons such as Star vs. The Forces of The Evil and Fineas and Ferb where girls are shown completing the task/work, yet it is the boys who are positioned as the ones giving the orders (for more examples, see Yücel Dinç, 2024 ). Such representations also enhance traditional divisions between the active, authoritative male and the passive, task-oriented female, thereby normalizing unequal gender dynamics for child audiences. The translated corpus of Disney Channel Türkiye, by contrast, showed no discernible gender differences. Although male characters were frequently portrayed as strong, active, and authoritative figures teaching or guiding others, there were notable exceptions that disrupted this pattern. For example, in Bluey , the father character actively participates in his daughters’ games and playfully engaging with them (for more examples, see Yücel Dinç, 2024 ). This type of example deconstructs stereotypical gender roles and provides positive gender lessons to the dual address (Alvstad, 2012 ). To sum up, channel is the main factor shaping activities (domestic, educational, professional, leisure). TRT Çocuk and Disney Channel Türkiye present systematically different distributions of what characters do. Age group (3–6 vs. 6–11) does not significantly affect representation in these categories. In other words, the channels do not tailor domestic tasks, education, professional, or leisure portrayals differently depending on whether the content is aimed at younger or older children. Overall Data on Appearance Below in Table 11 one can see comparison of gender, color and physical appearance according to channel and age groups. Table 11 Comparison of gender, color and physical appearance according to channel and age groups Chi-Square Tests Sig (p) Eta Correlation Cor (Sig) Channel Gender 6,654 0,012 0,128 0,128 0,010 Channel Color 34,776 0,000 0,292 0,093 0,061 Channel Body Type 257,344 0,000 0,797 -0,359 0,000 Age Gender 1,856 0,197 0,067 -0,067 0,174 Age Color 29,685 0,001 0,270 0,086 0,082 Age Body Type 116,444 0,000 0,536 0,349 0,000 In terms of physical appearance, Turkish children are exposed to similar gender portrayals in the dual corpus. Female characters are more likely to have a feminine-stereotyped color (pink), whereas male characters are more likely than female characters to have a masculine-stereotyped color (blue). These color codings function as visual shorthand for gender identity, making gender instantly recognizable to even the youngest viewers. A similar pattern was observed in the depiction of accessories: bows, hairpins, and makeup are frequently used to mark female characters, even in the case of nonhuman characters such as Akıllı Tavşan Momo [The Smart Rabbit Momo], Ciciki [Ciciki], Doru [Doru], Puppy Dog Pals [Bingo ve Rolly’nin Arkadaşları] (for more examples, see Yücel Dinç, 2024 ). Such practices illustrate how gender is not only embedded in human characters’ design but also imposed on anthropomorphic figures, thereby enhancing gender binaries through visual cues. When the colors that represent female and male characters according to channels were compared, there was no difference between the same gender (p = 0,392 > 0,05) or color usage (p = 0.143 > 0.05), but a significant difference was found when gender and color usage were compared (p = 0.025 < 0.05). For masculine depictions, blue is rated highest, followed by green in second place and gray in third place. With respect to female representation, pink is the first, blue is the second, and white is the third. One notable divergence from stereotypical norms is the portrayal of body type. In contrast to stereotypical body types, female characters are observed as average weight and height, similar to male characters, in the selected cartoons. This absence of extreme body ideals can be viewed as a positive departure from the hyper-feminized body shapes. Nonetheless, the persistence of gendered colors and accessories suggests that while certain stereotypes, such as unrealistic body proportions, are avoided, other subtler markers of gender difference remain deeply entrenched. These visual patterns, particularly when reinforced across both domestic and imported productions, may contribute to early internalization of gender codes, teaching children to read color and accessory use as natural indicators of femininity or masculinity. To sum up from translation studies perspective, it was observed that the translations remained largely faithful to the source material. While aggression and activities were translated accurately, the clearest examples of this fidelity were in speech behavior. This suggests that factors beyond the translator, such as institutional or editorial gatekeepers, have a significant influence on these representations. Regarding the study’s limitations, 1the analysis was based on 34 examples from two channels covering the period between 2015 and 2021. The study tried to present gender representation. For further studies, how children actually interpret or internalize gender lessons through original and translated audiovisual products can be a topic. Moreover, translation shifts identified between originals and translated versions may reflect technical constraints or production decisions rather than deliberate ideological choices. These considerations highlight opportunities for further interdisciplinary research that bridges translation studies, media studies, and early child education. 5 Conclusion This study examined gender representation in both original (TRT Çocuk) and translated (Disney Channel Türkiye) cartoons through a content analysis framework provided by Walsh and Leaper ( 2020 ) and Michel’s ( 1986 ) activity categorization. Across the dual corpus, the coding results do not significantly differ in terms of the number of characters, talking time or appearance between the original and translated corpus. However, meaningful variations emerged in speech behavior, aggressive behavior and activity types, as detailed in the findings. From the perspective of translation studies, these results suggest that translated content does not necessarily introduce foreign gender values or broaden children's perspectives. Instead, it often mirrors local gender norms due to deliberate selection practices and cultural adaptation. In this context, translation does not act as a transformative force for gender awareness among children but tends to reproduce familiar, socially accepted gender portrayals. When translated audiovisual products are exposed to such a gatekeeping, such as institutional policies, broadcaster preferences, and deliberate selection of suitable imports, gender portrayals are filtered to align with familiar cultural scripts. This process limits the potential of translation to disrupt established gender schemas and instead reinforces the cognitive frameworks that children already hold. In other words, translation becomes less of a transformative cultural bridge and more of a cultural mirror, reflecting rather than reshaping the gender norms of the receiving context. While audiovisual translation holds the capacity to broaden young viewers’ perspectives and expose them to diverse gender roles, its current implementation in children’s programs tends to maintain rather than challenge conventional portrayals. Future research could dive into the dynamics of this gatekeeping process, focusing on how institutional and editorial decisions determine the types of gender narratives that are imported, adapted, or omitted. Comparative studies could also explore audiovisual products designed for a dual address, assessing whether content aimed at both children and adults offers greater scope for challenging gender norms. Finally, examining domestic remakes and adaptations might also reveal how local production practices negotiate between global storytelling trends and national cultural expectations. Such interdisciplinary inquiry—linking translation studies, gender theory, and media—can deepen our understanding of how translated audiovisual products participate in the ongoing socialization of gender in globalized childhoods. Declarations Author Contribution F.Y.D. wrote the main manuscript. S.S.B. gave feedback at the each part and contributed to develop the main study. Finally, all authors reviewed the manuscript. Data Availability The datasets analysed during the current study are available in the Hacettepe University repository, https://openaccess.hacettepe.edu.tr/items/befb9107-1098-4571-bbb5-c54a225c2429. All data generated during this study are included in this published thesis. The datasets analysed during the current study are also publicly available in the ResearchGate link https://www.researchgate.net/publication/378550255_GENDER_REPRESENTATIONS_IN_TRANSLATED_AND_ORIGINAL_CARTOONS_A_TURKISH-ENGLISH_DUAL_CORPUS_COMPARATIVE_ANALYSIS References Alvstad C (2012) Children's literature and translation. Handbook of Translation Studies: Volume 1. John Benjamins Publishing Company, pp 22–27 Apap K (2014) Translation and manipulation in children's literature: Ian McEwan's The Daydreamer (Master's thesis, University of Malta) Bazzi S (2024) Dubbing feminist discourse in Disney films for Arab audiences: Frozen as a case study. Perspectives 32(1):31–42 Borodo M, Díaz-Cintas J (eds) (2025) eds. The Routledge Handbook of Translation and Young Audiences. Routledge Calbreath-Frasieur A (2016) Localizing Sesame Street: the cultural translation of the Muppets. Media Across Borders . 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Footnotes This article is a revised version of part of the master’s thesis titled Gender Representations in Translated and Original Cartoons: A Turkish-English Dual Corpus Comparative Analysis (2024, Hacettepe University, Ankara) See discussions of interventions in translating children’s literature and the moral or didactic motivations behind them (e.g., McMartin and Van Coillie, 2020 ; Pascua-Febles, 2010 among many others) Whole Day: Starting at 02:00 on the measurement day and continuing until the same time the next day, covering the 24-hour period. PT (Prime Time): The 3-hour period during which TV viewing is highest, defined as between 20:00 and 23:00. OPT (Off Prime Time): The time period between 07:00 and 20:00. (The data was derived from the website https://tiak.com.tr/tablolar .) Funding: Not applicable DATA AVAILABILITY STATEMENT The datasets analysed during the current study are available in the Hacettepe University repository, https://openaccess.hacettepe.edu.tr/items/befb9107-1098-4571-bbb5-c54a225c2429 . All data generated during this study are included in this published thesis. The datasets analysed during the current study are also publicly available in the ResearchGate link https://www.researchgate.net/publication/378550255_GENDER_REPRESENTATIONS_IN_TRANSLATED_AND_ORIGINAL_CARTOONS_A_TURKISH-ENGLISH_DUAL_CORPUS_COMPARATIVE_ANALYSIS ETHICAL APPROVAL This article does not contain any studies with human participants performed by any of the authors. INFORMED CONSENT This article does not contain any studies with human participants performed by any of the authors. Additional Declarations No competing interests reported. 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Dinç","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBAC9gY4i8HgAAODDZgtgU8LzwFmKIsZrCUNpsWAOC1A8jARWtjPH93ws+2wPD8z88bDBTXnow0OMB+8zcPwJx+nFp5ktpu9bYcNZzazFRyecex27oYDbMnWPAwGlg04tNgzJLPd4G07nGBwmMfgMG8DSAuPmTRQC06X8fA/Zrv5F6jFHqLlHFAL/zf8WiSS2W6DbWEGazkAsoWNgJbHZrdlzqUbzjgM9AvPseTcmYfZjC3nGBjjcVjis5tvyqzl+dubN3/mqbHL7Tve/PDGmwo53KEMAoxsyDxwROHXAAR/CCkYBaNgFIyCEQ0AGtpT0h8tGlsAAAAASUVORK5CYII=","orcid":"","institution":"Muğla University","correspondingAuthor":true,"prefix":"","firstName":"Fatma","middleName":"Yücel","lastName":"Dinç","suffix":""},{"id":542000101,"identity":"e390f959-c733-4a9c-9bf1-09a99a908d26","order_by":1,"name":"Sinem Sancaktaroğlu Bozkurt","email":"","orcid":"","institution":"Hacettepe University","correspondingAuthor":false,"prefix":"","firstName":"Sinem","middleName":"Sancaktaroğlu","lastName":"Bozkurt","suffix":""}],"badges":[],"createdAt":"2025-09-09 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21:39:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1676265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7573349/v1/79dea98c-f2cf-4140-a4d2-b387511509dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Representations in Original and Translated Audiovisual Products for Children in Turkish: A Corpus- based Study i","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChildren closely observe and internalize gender roles modeled in their families and social environments as they shape their identities through novel experiences. The original and translated content that children engage with significantly influence their understanding of gender. In many parts of the developing world, audiovisual products and their translations have become as influential, if not more so, than written texts. To this end, this study is conducted at the intersection of audiovisual translation, translation for children, and gender studies. The need for audiovisual translation practices has increased in recent years due to the rapid progress of technology and the shift from conventional media consumption habits to digital platforms. Children\u0026rsquo;s audiovisual content represents a crucial domain of audiovisual translation, combining elements of translating for children with considerations of media accessibility (O\u0026rsquo;Hagan, 2012; P\u0026eacute;rez-Gonz\u0026aacute;lez, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and cultural adaptation (Klingberg, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Oittinen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) among many other fields of study. In addition, scholars such as Oittinen (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and O\u0026rsquo;Sullivan (2005) have explored the unique challenges posed by translating children\u0026rsquo;s texts, emphasizing the interplay between verbal and visual narratives. Since children's television shows have a global audience and a significant impact on young people, their adaptation has also drawn the attention of scholars (Moran, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schober \u0026amp; Olson, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In particular, gender representation within these translations has emerged as an essential aspect of research, with scholars such as Di Giovanni (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Josephy-Hern\u0026aacute;ndez (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examining how translation choices can reinforce or challenge gender norms.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAs the nexus of translation for children, gender studies and audiovisual translation, this study aims to unveil gender representation that children aged 3\u0026ndash;11 years in T\u0026uuml;rkiye were exposed to between 2015 and 2021 by comparing the original and translated corpus from two widely viewed digital TV channels, namely TRT \u0026Ccedil;ocuk channel and the Disney Channel T\u0026uuml;rkiye. Additionally, it seeks to shed light on how female and male genders are portrayed for a specific age range in both translated and original audiovisual products. Determining whether children are exposed to similar and contrasting gender-related images and messages\u0026mdash;and examining their nature, frequency, and potential impact\u0026mdash;is another goal of the study. Another focus of the study is to reveal the similarities and differences between the translated and original audiovisual corpus.\u003c/p\u003e\u003cp\u003eA total of one hundred and thirty-four examples from the dual corpus was analyzed to identify gender stereotypes and speeches. First of all, the original cartoons of TRT \u0026Ccedil;ocuk was coded with the content analysis method suggested by Walsh and Leaper (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Secondly, the translated corpus was coded in the same way. During the coding procedure of the translated corpus, the English versions were compared with the Turkish dubbings whether there was a translation shift in terms of gender representation.\u003c/p\u003e\u003cp\u003eThe content analysis method suggested by Walsh and Leaper (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in \u0026ldquo;Content Analysis of Gender Representations in Preschool Children\u0026rsquo;s Television\u0026rdquo; was used for the coding procedure; the number of characters, talkativeness, speech behaviors, aggressive behaviors, activities, and appearances were examined in the thirty cartoons, composed of both original and translated audiovisual products. Moreover, the categorization suggested by Michel (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), in his \u0026ldquo;Eliminating Sexism from Children\u0026rsquo;s Literature and School Textbooks\u0026rdquo; was also used to expand specific operational definitions for coding gender-stereotyped activities.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2 Theoretical Considerations","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe intersection of gender, translating for children and audiovisual translation brings about an interdisciplinary approach that focuses on the translated audiovisual products for children from gender perspective. These disciplines were selected for their relevance to compare original and translated cartoons in Turkish, offering a lens through which to reveal the similarities and disparities in gender representation. The discussion proceeds by first addressing translating audiovisual products for children, then extending to the translating gender-related issues in such content.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Translating Audiovisual Products for Children\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhile children\u0026rsquo;s literature was long overlooked in academia, recent decades have witnessed a growing body of research dedicated to both children\u0026rsquo;s literature and its translation. Worldwide, translations make up a considerable amount of the literature that children read and (or) listen. Gillian Lathey (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, p. 2) claims that translated works created for children promote global peace by helping children comprehend different cultures and nations. Thus, the translation of children\u0026rsquo;s literature has recently become a rich area of scholarly investigation, addressed in different peer-reviewed journals such as \u003cem\u003eBookbird: A Journal of International Children\u0026rsquo;s Literature\u003c/em\u003e and \u003cem\u003eChildren\u0026rsquo;s Literature in Education\u003c/em\u003e, as well as master and PhD dissertations (e.g., Apap, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zoli, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), among many other academic works. Foundational studies by scholars such as O\u0026rsquo;Sullivan (2000) and Oittinen (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) have also deepened our understanding of the interplay between text, image, and ideology in translated children's books. These studies also highlight the difficult choices translators make when adapting texts for young audiences.\u003c/p\u003e\u003cp\u003eIn the Turkish context, valuable contributions have also been made on the historical development of children\u0026rsquo;s literature (Neydim, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), characteristics and challenges of children\u0026rsquo;s literature and translation (Erten, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), translating culture in children\u0026rsquo;s literature (Hast\u0026uuml;rkoğlu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kansu-Yetkiner \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), translating sound in children\u0026rsquo;s literature (Din\u0026ccedil;kan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) alongside many other notable studies in the field.\u003c/p\u003e\u003cp\u003eFurthermore, the increase in television programs, game shows, cartoons, animations, puppet shows, films and videos aimed at children, in addition to books, has made it possible to examine child-oriented translation products under the subfield of translation studies, namely audiovisual translation. Consequently, core issues traditionally associated with children's literature such as dual address problem (O\u0026rsquo;Sullivan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), relationship between written and visual materials (Oittinen, et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), didactic concerns (Van Collie \u0026amp; Walter, 2014) can now also be extended to the realm of translating for children in audiovisual context. In other words, the characteristics of audiovisual products translated for children exhibit strong parallelism with those of the research categorized for the translation of children's literature.\u003c/p\u003e\u003cp\u003eThus, the abovementioned traits\u0026mdash;cultural context adaptation (Koigeldiyeva, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), ideological manipulation (Lorenzo, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), features of orality (Varga, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), text and image (Masero, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the dual addressee and such\u0026mdash;can be observed in studies at the intersection between audiovisual translation and translating for children.\u003c/p\u003e\u003cp\u003eChildren's programs, which have the abovementioned characteristics, include many television shows, such as puppet shows, advertisements, game shows, cartoons, animations, films and videos. As television culture in the USA developed in the 1950s, children's audiences became a part of an entertainment-focused society (Schober \u0026amp; Olson, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In 1969, for example, Joan Ganz Cooney and Lloyd Morrisett created the educational children's television series \u003cem\u003eSesame Street\u003c/em\u003e, which was produced by Sesame Workshop and teaches American preschoolers about the alphabet, numbers, health and safety, and other subjects that are pertinent to children's everyday lives. In addition, children from South Africa, India, and Indonesia around the world have the opportunity to learn from and enjoy distinctive Muppet characters, which are occasionally widely shared and other times extensively localized (Calbreath-Frasieur, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, p.99). According to Moran (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), \"children living in Europe, Asia, Latin America, truly almost anywhere in the world, have the opportunity to watch an episode of Rugrats, Power Rangers, or a version of Sesame Street from their living room or broadcast in the original English, dubbed into their native language\" (p. 288). As Okyayuz (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) stated, the \u003cem\u003eMuppet Show\u003c/em\u003e, one of the first audiovisual products presented to child receivers in T\u0026uuml;rkiye, was adapted into Turkish sense of humour with the contributions of the translators (p. 138). These worldwide circulating audiovisual materials illustrate not only the cultural adaptability of children's programs but also the growing demand for localized, linguistically accessible content. This widespread international distribution is often facilitated through translation, which becomes a practical necessity rather than a creative luxury. The exorbitant cost of creating high-quality animations for a child audience may be why many nations require and favor translated audiovisual products.\u003c/p\u003e\u003cp\u003eWhen it comes to translating children's programs, there can be several reasons why dubbing is more commonly used than subtitling. Dubbing offers accessibility for young audiences (Borodo \u0026amp; D\u0026iacute;az-Cintas, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), it creates the impression of a domestic product, and remains suitable for both children and adults who are not yet literate. However, despite these advantages, dubbing also presents certain challenges. It is important to anticipate the unforeseen issues that may arise from translators\u0026rsquo; decisions since the target child receiver brings with them pedagogical sensitivity. If translation decisions are not the target child receiver oriented, all decision makers such as producers, editors, audiovisual production specialists and translators may encounter ethical and functional dilemmas. In this context, cultural adaptation becomes vital.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Translating Gender-related Issues in Audiovisual Products for Children\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGiven that this study focuses on the gender representation of the original and translated children\u0026rsquo;s programs, it is necessary to define gender representation. In its broadest sense, gender representation concerns how men and women\u0026mdash;and, in this study, girls and boys\u0026mdash;are portrayed. In society, gender classifications are established through stereotypes based on expected values, traits, and behaviors (Lott \u0026amp; Maluso, 1993). Michel (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) stated that \"stereotypes can refer to the physical characteristics of a group (e.g., women or men), the intellectual, emotional, or volitional qualities of its members, or certain aspects of its position in society\" (p. 16). For example, the statement \"men are aggressive\" is sexist since it suggests that males are naturally violent. The significance of early childhood education is overlooked in this statement, as it encourages boys to channel their aggression onto others while encouraging girls to suppress this aggressive emotion or conduct (Michel, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, p. 16). \u0026Ouml;zdoğan observes (2025) that \"gender, as a fundamental aspect of human existence, has implications on how individuals perceive themselves and their roles in society (p.30)\". These perceptions are not innate but socially constructed, and they are permanently reinforced through cultural products consumed in early childhood.\u003c/p\u003e\u003cp\u003eRepresentations of gendered behavior appear not only in written materials such as picture books, school books, children\u0026rsquo;s magazines and story books, but also in audiovisual products such as children's television programs. In both cases, these materials contribute the internalization of gender norms, demonstrating the need to scrutinize how translation practices may either perpetuate or challenge such representations. Thus, in the realm of translating children's literature through a gender lens, numerous studies, research and theses have been conducted around the world and in T\u0026uuml;rkiye on different subjects such as translating gender-related content (Chen \u0026amp; Song, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), gender roles in children\u0026rsquo;s books (\u0026Ccedil;elik, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), feminist translating strategies used in marginal fairy tales (Dağabak, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), translated feminist children\u0026rsquo;s literature (Demirhan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and gender representation in picture books (Gooden \u0026amp; Gooden, 2001) among many others.\u003c/p\u003e\u003cp\u003eGiven that gender-focused themes in children's literature can shape young readers' understanding of gender roles, translation plays a critical role in either challenging or reinforcing existing stereotypes. According to Taguchi (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), \"Poststructural thinking focuses children\u0026rsquo;s own active ways of acquiring what they regard as suitable gender positions for themselves, which means that different gender positions could be desirable for children depending on powerful discourses in their context\" (275). Therefore, gender representations play a significant role in the books and cartoons children are exposed to or presented with. In other words, translated versions of written or audiovisual materials, as well as their original versions, are among the factors that shape children's perceptions of gender. In her study, Demirhan (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) underlines that \"source texts are not gender-friendly all the time. When translating patriarchal and androcentric children\u0026rsquo;s literature, by revising and adapting the source texts from a gender positive lens, translators\u0026rsquo; interventions in gendered and anti-feminist ideas has become apparent\u0026rdquo; (154). In other words, by intervening against sexist and anti-feminist notions, it is possible that\u003csup\u003eii\u003c/sup\u003e translators can make dubbed and subtitled materials gender-friendly in contrast to their originals while translators adapt these works to the needs of children and the culture, they live in. While much of the foundational research on gender representation and translation has centered on children's literature, recent scholars have started to extend these concerns to audiovisual translation, which is also a highly influential medium in shaping children's gender perceptions such as dubbing feminist discourse (Bazzi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), shifts in audiovisual translation for children (Di Giovanni, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), gender translation of an anime film (Josephy-Hern\u0026aacute;ndez, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and gender stereotypes in animated cartoons (Tomoiagă, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) among many others. A common point across these studies is the depiction of children's gendered bodies and behaviors, as well as their talks in the source language and the translations into the target languages. Children's perception of gender identity is shaped by the patterns in the audiovisual materials they consume. Therefore, the young audience's cognitive perception is influenced by the audiovisual product's narrative and language, whether or not it is sexist, and how egalitarian the discourses in gender roles like mother-father and sister-brother are. This is why, as explored in this study, it is essential to examine how audiovisual products that teach children gender lessons are conveyed through translation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThis study engages in a content analysis of a total of one hundred and thirty-four examples from two main television channels, TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye. The content analysis method suggested by Walsh and Leaper (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the activity categorization of Michel (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e) were employed for the coding procedure as previously mentioned in introduction. Each process is explained in detail below.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Limitation\u003c/h2\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe study began with the selection of two widely viewed and preferred children\u0026apos;s channels, one of which aired mostly domestic Turkish content (TRT \u0026Ccedil;ocuk) and the other of which mostly aired foreign content (Disney Channel T\u0026uuml;rkiye). The channel selection process can be briefly explained as follows in the Table\u0026nbsp;1:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Viewing the measurements of the children\u0026rsquo;s TV channels provided by TİAK [the TV Audience Research Company].\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eIn terms of viewing rates of Audience Research Company \u003cstrong\u003e[\u003c/strong\u003eTİAK\u003cstrong\u003e-\u003c/strong\u003eTelevizyon İzleme Araştırma Anonim Şirketi\u003cstrong\u003e]\u003c/strong\u003e which is a joint stock corporation, TRT \u0026Ccedil;ocuk is ranked highest, followed by Cartoon Network, as shown in Table 1. Using the years 2015\u0026ndash;2021 as the basis, TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye were selected for their wide recognition, diverse content, and, most importantly, their steady position within the top four.\u003c/p\u003e\n \u003cp\u003eIt is also necessary to provide a brief overview of TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye. TRT \u0026Ccedil;ocuk, affiliated with the Turkish Radio and Television Corporation (TRT), is an official channel and the first local children\u0026rsquo;s channel in T\u0026uuml;rkiye, aired on November 1, 2008. It highlights that it provides learning-focused and educational content for preschoolers and school-age children (aged 3\u0026ndash;11) and strives to foster the target audience\u0026apos;s intellectual growth. Most of the cartoons and children\u0026apos;s programs that air on TRT \u0026Ccedil;ocuk are produced domestically, with very few translated products, according to an analysis of the network\u0026apos;s weekly broadcast flow (for further information, see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trtcocuk.net.tr/\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trtcocuk.net.tr/yayin-akisi/\u003c/span\u003e\u003c/span\u003e). The Disney Channel, an American basic cable and satellite television network, was established in April 1983. The Disney Channel Worldwide, owned by Walt Disney Television, is a global entertainment portfolio with 118 child-driven, family-friendly channels and/or channel feeds accessible in 34 languages and more than 164 countries. With respect to the introduction of Disney audiovisual products in T\u0026uuml;rkiye, the first Disney Channel was launched in 2007 by Digiturk, a subscription-based Turkish satellite service. On January 12, 2012, it began broadcasting as a free-to-air channel via the T\u0026uuml;rksat 2A satellite. Ten years later, the Disney Channel T\u0026uuml;rkiye announced on its Instagram page that it would shut down. The broadcast was stopped on March 31, 2022. The majority of Disney-made content is currently accessible in T\u0026uuml;rkiye through Disney+. For this study, all cartoons were watched and analyzed through TRT \u0026Ccedil;ocuk YouTube and Disney Channel T\u0026uuml;rkiye on YouTube.\u003c/p\u003e\n \u003cp\u003eAfter deciding on the channels to be examined for the study, 30 television series aired on these channels were selected, comprising 21 from TRT \u0026Ccedil;ocuk and 9 from Disney Channel T\u0026uuml;rkiye. The selected cartoons are decided based on age range of the viewers and distribution of female and male characters as well as the distribution of human and non-human characters. Below (in Table\u0026nbsp;2 and Table\u0026nbsp;3) you can see the details of the selected original cartoons aired on TRT \u0026Ccedil;ocuk.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e Original TV series for the age range between 3 and 6 (aired on TRT \u0026Ccedil;ocuk)\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e Original TV series for the age range between 6 and 11 (aired on TRT \u0026Ccedil;ocuk)\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eThe Turkish-made TRT \u0026Ccedil;ocuk cartoons listed above on the Tables 2 and 3 include \u003cem\u003eAkıllı Tavşan Momo [The Smart Rabbit Momo], Aslan [Aslan], Ciciki [Ciciki], Doru [Doru], Ege ile Gaga [Ege and Gaga], Elif ve Arkadaşları [Elif and Her Friends], Elif\u0026rsquo;in D\u0026uuml;şleri [Elif\u0026rsquo;s Dreams], Erdem [Erdem], Kaptan Pengu ve Arkadaşları [Captain Pengu and Friends], Kare [Square], Kuzucuk [Kuzucuk], Niloya [Niloya], Pırıl [Pırıl], Yade Yade [Yade Yade], Bulmaca Kulesi [Puzzle Tower], Emiray [Emiray], İbi [İbi], Maysa ve Bulut [Maysa and Bulut], Nasreddin Hoca Zaman Yolcusu [Nasreddin Hodja, Time Traveler], Rafadan Tayfa [Rafadan Tayfa]\u003c/em\u003e and \u003cem\u003eZ Takımı [Team Z\u003c/em\u003e]. These cartoons generally focus on the adventures of the main characters (e.g., \u003cem\u003eThe Smart Rabbit Momo)\u003c/em\u003e, school and family dynamics (e.g., \u003cem\u003eElif and Her Friends\u003c/em\u003e), promoting environmental awareness (e.g., \u003cem\u003eCaptain Pengu and Friends\u003c/em\u003e), and conveying traditional and moral lessons to children (e.g., \u003cem\u003eMaysa and Bulut, Ciciki\u003c/em\u003e).\u003c/p\u003e\n \u003cp\u003eAs for the selected translations aired on Disney Chanel, same parameters were valid. Below (in Table\u0026nbsp;4) you can see the details of the selected translated cartoons aired on Disney Channel.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e Translated television series for the age range between 3 and 11 years (Disney Channel T\u0026uuml;rkiye)\u003c/p\u003e\n \u003cp\u003e\u003cimg 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9WnEY9dmYVXf+4ddkq684eQ0Yux12Wi/XSjj3UMvTANvOzONfOy69MCIK9KrBWNeeuvu/BxzAMY+e1M2UEBd3a4rHJBb7z4nvfq+WabjeNAC/r5VWsDf8dIC/r4B/ArwfOa127PNfuHtuwpb+XCeOM3GPv3yrcX/I9LYIji78bYz0rBHrirs5TXpyZduTWML+zn80avTG/8akp4srnuwwIXXP7o/PVfY9AceviLberP+7x12cRp4+xlpxOhr07CRV6WnXrk1jXr8ujT66YHp7c+GpSdfvCXd++Al6bahA9Izr8OMh1vAX+hejwJ+4zLeZ1amCDvGaSwJMZbknOO1TX3GfnG/5xJcHqhrXOu8+xihccfG5OsUx9wfGwR5Rhh0S0PieET6UToL/J5N0CjU+ICjdt34gcX58T2jrTKhZzcD+L3Tc7W96nwU57vKl6pSu2/8zx5fcV8zgV/d6ktHnl9r339eP6HndPQ9HSme2yzgr7X3y3VvD986qzMdKX0R+Nnomk2ubbLmf33P3jqO57ICYY8dZz/ZWO90nnw4rg/ZHL89w/9fttu1ZxrDd7/32/tFpth559ozth/F8zsD/OocMlcuzZav9hb90KOAv3xt1fG2iusJKgDOyvd551c9p6PPri/u7wjwu55AhbC6N3a1qrqe8LbFlyjO50xGxbnxFQo2vmfjXaOAnxJoq3Z7p77X/lr/fPlaRrnWd23LmrpMiC9VxTPVY3zPHl/RjmYBv3Zrk7bjTRQyUHV9VVE3zygDa/CzLd11rfPNyujgD343A/jxpsajmiwwuDW+td3H3g0gOsLXzpQs230M+KPU63T8z/bpn5q9sv6eXRiZ+yquYa/LgF31u/75jShkpzPAHzYl9DXkr1n61FHb12OBv6NFHR9+4prU/5z904gnTcSrGT0dRNkbmcLRjvYCP6HNwvDJg+nmwWenQ4/dMe17yNbp0utOSC+/c2+uGyOpftG5ZrOee+kReWMKgum467TFeZsXWSJzzS2n5DY6T0ldk9/1+bvdyzgqnm/jjGtuPSWvfLBfQZWRbBTw10B/ZBr74k3pjAsOyZN3jOuZPxFtUXeFY/DEq7fkOR33jbykeKfztfbU+m9k3lzpxjsL4C3a/fIH9xR8GPcM12lrvDeA0PkPivLYCwPTOZccnoY/fk1+bn1dJ1TimY0Gfu164qWbU79CZo84YZfU7+z90omn753XRF8x8KTcZ8F/xtJv79UG57RRvcx7ufDqY9PL7w7+whg98fLNWRfuefiifA1euB6v3Pvie3cXBv7IdOcDFxT31ByNRrQpSq2ejQV+7X7rXw/k3eDw6db7z03vFXJhXo914aedd1Aa8cS1X2pvTTeG5/aecf7B6cbi3uCB4yFjVe/rTMl17KPAX1/ILL0kg6edf2B69Lkbi4BnTBrzwqB09qWHpYefpI+i+lrEHzLIZrqv3Ef0vOodXS3e01HgV59Rz1yfJ5ofedJuqf/Z+2fbdviJO6eBd5+R60ru6GI8T7v8Vmp6HPa51jYl5LDGj3G23PGX370n84/8t0dPPa/XAz9GYPJVN/VLv1n81+maQf1zaokBcBzoEiz/V93f0dIR4Ndp7308PB1y9I55Q4sZZ/llmnveWdL3J/tu+udmq6cX37r787rVBOXdT4enHXffMP35L0ulF9+9u1BGqa1CUIo2AFJG/Zk3bk+rrL5M2mG3DdKH/66BnPe/+2kNHOJ5eP3Gh0PTm0WRCnu3qMuSf1g4r3h49qU7arz5j/o2BvjV6cHRV6TFl1owff8H3817K0wx1c/ypM3LC8dD3TzbdR//69F017AL8mTK0wYcVEjfE7k9b338wOdte6Ro/0N5g6WVV186j/F98m+Zk8/79JNa/1Mqz333X8PSa+/dn+9N6cl02TUnph/95PvphFP3rj2rHYpTLq73nkYCPxkio/c/fGlaYZUl0syzTpv+93//X+bVXL+aJW2/+wYZ1Gwc5XqG4v1/194bToC2vlfw5W/rr5gnNz313K1Fe59K737wUAHo56ff/X6+1P/M/TI/8QdPyJH/Ga6f/vxHaesd1sv1eL94j2u62q4onkP2GwX8NZ0bmd74YGj6y1+XzTPIydYrr92Xrrvl1Lxr3P/72v9Lp555QNG+Z3Pfa+97Bc+kiTmWf1j+t2mPAzbL7RQYuObtj8lq4yK1FvCPKxkAC7na64Atcn+tt/Gq6eOPn0g33nR6muyH3ykwYN+cxmfj2Oh3CvslE0qPje3rkyyT2X6PyKWcCWhE6Qzwq+NN95yVlips6XTTT5X++7//O/30Zz/MS8IPOXqHQt7GZpv92rt2iK3Z53eL9ng2PXjrE8GggOeRLH+1a4rg5fNr8lLEwvbhH968/9nDaYfCHqz5tz+mF1+FFxPGsT4D/Bh6SxEB/KMQrnsevjgNHn5h2nmvjfJ60TXWWT7tvPdG6fEi+mxPx06otBf4CSnAHXj7mekHP/5+3p3qkbE3pBdfu7foyA3T/xaG6rSzD8wTE9XP0sENN/tL2m6X9fP/b/5raI5gbENpSeGu+26Sdt9/0zS48KB3Kc6Leh5+8tq0236bpr0O2iJvOLTpNn/NmY9XCg/xkGN2zGtdVy8ERjT0ygf35Z2t5p2vucBPmT4oDK76fONbX8/fUXjj3QfTbYMHpF9OP0VeoaGOR56wa/4Ww3obr5Ij3o22WCMb8TfeG1LUfYe0ylrLpB333DDtuMc/03W3n5YjY07RM2/ekc686JDMo212WS+379Sifa+9PyRdeNUxaa2CV1Z47HnQ5un5l+4uHMFT8hLPSQn4Fc9449Oh6dW37k93D7kg82b1tZdLz714Z3rtnfvy/hT7HrZV0a77soOw7S7/KIzT5emyG05IW++4blp/k9XSfoduk1emWKJqw5MDD982HXDUtumOoedlfl5148np+VfuSgcdvX1ae/2VCidhpXTJtcenkU9dl6b+5S+ys7Vh4YCuu9Eq6db7zs1GpVFtazzwjyqAf0gB/MtlIPlx0ad3D70o7V7ouP+/9e1v5m96PPnMHdlx+nPhJNKH+0ddml545660ZaFHp5xzQLrhztPy+V322Tg7EZb4PvfmnQ2xcy3gH1cC+Pc7ZOvcP1/7+v+mCy87Nt12z3lF302W+wKIysatvMbSadW//iGdfdHhhVNfAOtdZxX6/c/C5tX6aK8Dt0hPvXxrBkVywAnkAFe9tyOlM8BPtmVjX3t7aLrs6hPSjwvQ367QzRdeuSe9XsjnDbefXujTyrlN2+y0Xhr5xHXp/pGXph322CCtv+lf0pY7rZNGPn1dDnIsM99u13/k+y+57rjsCJx+/sFZxjfcfPVC7y9LN95+Rl7qaFO5o0/YLb3/r9r3DarqFqXPAL/nnnXRYel7k30nXTnopHTaBQdnYbNEb42/LZ++8c2v5+0kc2TcRYFuL/Crlw2Jjjh+l6IuX01nXnhI8f/TuQwvgM8OhFtu97ccAavrNNNOkTbdaq3028XnyztUjXn5pgwEvMntdvtHzhTY3eq8y4/M5zk5N9x5er53gUXmzgbc1sY77bFheuixqwpn4U9p7fVWTHPPM3P6yc9/mAYVXiqBEn0/+3LzgN890vEzzz5dfvfbRQQuylS23mnd9OMfT1YYlyOKyHbmvOnH3zdYOe190JZ5J67j+u2Zt2T+xre+kf627kpp5b8sk/7f//5PYZy3TJsUvJGtGP3ioGwMtPufm66eHZmpfzl5um3IgOzsrfyXpWv7Mnz1K0X/7JNuuOP0NPkUP8nPnZSAXwkZGfvsoDRjEbXbVlrEwMDpv2kLZ+Cl1wanCy46Kn31v/4rXXzNcXlDE21feNFfpSML2SIHU00zeVpsqfnTLLNNl869+Ig08K4z0w9/NFk66qRdU7+z9s/XL7nMImmFlZfIvB429srMs+lmmDJtts3aefOUFVf9fXr1nXuzUSXjVfVtb9GuZgA/w7py4dQBfRmLPQ/cPC/zmqZwYn42+Y+K/jk83XTnWelPqy6Z1vz78nkjsL+v/+f06FM35N3kbP+6f+Ec4cdf1lwu/Wml36evf+P/0oBCpxphG1rAP64E8MvU/fBH3y/s2y/SfAvOkQ4/Yef0y+mmSGcUNtpyvSmn+nlaaZUl0worLl5c9710SQGmRx63W87i2NFPdvNrX/vfApz3TO9+/FB6/IWb84qv1wvw7aoedgb4lZrePpXuKJwYtoVOsW/aa++C5Yq2CDjtQWD799PPPyjvRWMfgt333jRvbGSDoz+vsnRab4NVClv11cIx/XsacOmRWZfJLzvNrp981n55KSR573fGfhn4J+T09CngP+fiw/MWvoCf1/T1b3wtXXXdyXmpyJTT/Dxtv8sG2Xh0F/ArjPoJp+1dCPF/pROKCF3qGfDfO/LivKHQtjuvl8dz1fWIQhg++WxMWmudP2UFuf2B89JPCtD/+z9XLu55Ix186Pbpu9//TjZSzov4AL97jz5h9xyt2XdAlM/QiYTW3fDPacFF5spbYF54zbFpzXVW6Bbg90GNX803a9474ZX3783tZlj+ufkahSPzo3Th1Uen+RaaI61YKPyHnzySbrv/3MKrnSwdXESmDPbMs0+b3njnofTAI1elKab6aY7KGG07MT6WgX+54tnTphdfvS/1P2P/zKerbu6XTilAbp0iqv3zaktl477/4duk624/Nf1iyp9OksAfOjHy8WvTDDNPUzhBf04ffTYyp/fW/ecqafa5Z8yRxaVXnJC+9Z1v5vkZWxfA//Nf/DhHA5+lMYUjsE5uqyKdTRbvfvDCrAtHHr9ruv3eAXktNKdg0d/NmzdDGfzQRXno5Z+brFE847H0l8LB/NWvZ8kyLQpTr6r6trfgT7OA39DI734/f1p8yQVy1gIv/r7hymnKqX+Wzjrv0DR89DV5i9e1CsfpRz/+QVruT4ulhx+7Nu/QaRfP/QqZsIXsvfdfmoY8fGX+mIzsWCNsQwv4x5UA/p33/GcOWk46c5+87S4AE8EKhDiiM8w8dXrrk2FFxHxv3guAvTvQTn6FPTCfY9Sz1+eMnYzXAUdum+b41Uzpt0X/3z38wqw7XZGrzgJ/LZv7WLrpjrOy/NEp8mN4jjPzt8LZZGu/XeisgMU+Bz/7xY/S6QMOLvg8trBla6Uf//wHaezTt6UXC4ydcdZpsgO+/iarFsHbN/L+NH9Y/jfp55P/OGcx8WmJpRZK73xQmztVVady6VPAf9aFh+YoGpD2P+eAHP0OuuWs9NTLt2WvbLud12+IcocRak+qn1G996FL89j27HPNmG689Yz04MirMnAxPldd3y9ddvWJGbxNUOJF/mWtZXM0PPTRy9Kcxd/FllowDRlxdY5cvvO9b6Xzrzgqf7sAMFx76ym5naeceUDBy1EZbD37+FP3zF71ToXSAcJvF3y5qIgWOQWe3dxUf004t935Hzma2nWfTdLoJwcWRvmQ7ICsVIDy0NGXp9nmnCFtsOlfijY/mW659+zcd4cdt1PaZqd1M5Bffd0p6bj+e9W+zFgY6422WDN/t2H0CwMzsP96wdnTa288WAD/Abl/jygiiemLqG7l1ZbJKXBj5vseunXBo/7ZcBxfPGtSBf4RY6/JEZEo/8MC+OnFJoVx+EXh9Ehn739QLV16ReHUMhCiJKlP2YF/bLxK+vFPfpB3LQOEp5xzYE7bT1YA/wGHbpvueeCinPLeafd/pgUWmjvPLzn5zP3yNyu233H9LKM+dsI5ePy5myd54F9m+UXTsn/6Xc5sGV8VIRkqmvwXPynsyr5p1703STPMNE0ReW1f/J06Lf3H3+Q9Pqabfsq8P7xhMXMpHnr42jR0+BVZ1ozNNsI2tIB/XAngN3Q53YxTFfZkUDr2xD2+cFJNLhW8TD7FT9MdQ85PA289O2dyDIMefNQOeU7UXcPOTyOfuS5nDGykduWgftmu7FVE2OapkKuqd7e3dBX4B912ZnZiDL0a1n38xZuzbfahoaNO3i39rHByZGJPPGOfQs5+kK4e1L/Q2Wdypoq9O+f8I9NFVxyfM7KyoQr9JMOK783cPezCvMHcr349a/7WQXsws88AP6W94Mqj8zgpMASik0/5k6Jjzsozp0VOtokEjt0F/Ip3ieCkcGadc/o01S8nz3WcojDc0kOffDq26Pjj0s+n+HHe05owAXS7UL347l15xqiJW4suNk8Gysl+8L10wVVH549UMOZmKf+iAL3Tzz0oG1pfqrKzoVUB0rgmO/5msV/naOj0Cw5Ofys8SUMJz77S3Ml9sg9PvnxLbgvhtz+3yN27hwy/LD33xp35a4q2S2Zwbh8yIPfXyWfsm+4bfkla+Le/SnPOPVPeffG73/t23n6Z4i++1ALpsZcG5UzG4sssmF5+/f6i7Qfnnb5OHXBA/kaDyM5OX8CQgl19S7/M+1POPiC3eVIDfrIz8snr8vDNRpuvkd77tDap5/zLj86ZCs7cQkW7GL/r7zgtG0byPLYwpBwZe56bNzHqqRvS2uuumOZfdK505sWHphlnmSYde/Ie6bqbT61FSQXvOZJL/WGRvHJktrlmSLvuuUn66N8ji35aOe+K9uQLt2QdVa+q+ra3NAP46e7rH9yft2r1PY3Lbjwx97tsxaXXH1/7bkeh94cdu1MRXf04f71TFkUWZMioy/KQkKjRypppCwdx2MNX5y/GzTLHdOnY/ns0xDa0gH9cgRsmje5V6CAZ5vy/98nDeVz7e9//Trr42uPSQ6OvynOfBEbTFvZqiSLIGf3EoHRC/32yrbzzwQvSCPNRCqeYvfx3ejxPaKaPjdDBrgH/6HTL3Wdn+7xfEWDQ2WdfvyPLnQyz3V9l1Tjmsr3stwno/05PpCGPXJ6WWW7R/IEhmatvFVG+sf57C9v3+2UWyiA/e3G9TNbzb96Z56fQ/822Xjt/tXBCmNwngF/RgU+/flvet//Zt+5IT756a456nn/7rjwWdMcD56dHnrkhX9cVA6RoR3uBX8ngX7TzsWdvTJddd0K6oDDoQ0ZdnvkqMn7qtaKuhYH0sQrG8qGxV6X7h1+aXnp7cBEB75xW+stS6eAiIll+xRqYmbE97NGr0vDHr03Pv3NXbucTr9ySFeGe4RenYWOuSm8WxueuBy7IHzxyzD2PFB7yg49dke59+OL0xidDK41co4BfAf4mr/ksp/SXiXt25QJW+oQn6xsI2ux727fff156+sXb0xU3npRWLJRmh103yIaakTjxtL3T48/cnOvu3gcfuzLdO+KSbGS13WQ2S7Z81OOCK47O77xn+EX5OnKh/y1xq2rzhEozgV/B81c/vC9vFz388avzd8vJhrbdOfT8dPn1J2Tg0p/Pvnl7nhjkNz641z13P3Rhevezh7KzZXx/5FPXZp6MeeGm9O6/HioMzWX5E9aXX39ieuaN29Kb/xpSnD8/8wtPho0p+Jl5OyQ/s6qeHSmNBn5FPS1JfeDRywsjfXne2c2QhYmKz719Z57jYQe51z64L91w+2l5bsd9hYwMHnZRYQfuLP5eWIDItTljZDvYV967N+8wh0+WnXp+V+uIdy3grxUyoDzy7PV51c4rBRDRfZOsbxp8VrbR7OjTrxc6f8NJeYnzU8Ux81ss+bvtvgGFTg9Or3x0b7brjzx7Q6EXlsTVlr41QgezTHUC+JVstwr7S5Yefa6GLeo29qWb0sVXHZcG3nlmuu/hSwoZvDSNfv7GbIOfe6tm/26+5+zCAfpD2nK7ddJeB2yeJvvhd/NwgRU2T75ya7roqmPTpdceX9isWzJG4BkekVU6MKG2q0ePBX7Xl0vVNeWiIzzfXwWDQ5kBMxDjqVXd25GiLhMC/vr36ChtlUZVRBfZSBTXRV39dS0jKfK3xOXok3dLc/96ljweLiLc5+Atc9TDuwSsnlG7tyawnut+7VYn/FBX12i/Oowv6vW8RgG/Em3TZgodz9Mn6qENeKA+zmvX1Tf3z0vR5pl/1lzW+vvyaXReA1yb0ete92mrZ1FWbdVmy2Jq19WWAQLsWh3a3sxmQkXdugL87ZFjz9O28qZOtT585PM+rLVH/+Ch9kQd3BN9WjtX62t/8cZxvMLf6IPacMy4ZWwTkouOFs/B9wkBf5kfbfGmvqirEv1a04Pab3/xCM/8r2hXtNe1NXmpDfnE8Y4Y/PGVrNMNAv6wIe3lS1eKdzQa+KOU5VWb6BI51FfO65Nx9qG2yY/+0Ic1+10byg1ZbWTx/PYCf1U/4FHoWxzzO2xVTT7toxKbiD2U/3JCl/3TbwvbPmsesvR12ftHXlacq+27UraXNZ7Vnknuy+9vqzQc+CmvCmis/1XKeccwMa6rKhinA9sD/J5vYlxt29xaAYY1444xozMDCWvuvM+VRPHbNeUtJB13nWeoQ1eUyb3eXQ/88R7P956uGlFCQhDHPD8o3T3kwjTy8euysoTCNKN4ZyOBf0IFj4D9uH7W58+kdz5+sODrNWn0EwPTm58M/VwhGIWaoda36lru92YVdews8JOLejmu1X3crmWNKPigrzx7fH3mnBK6gJeNrEcU/NHG8QG/axi36Hc2xLFy3crXN6OoI7lqb39OqGSZbADw1+xX6IM+nfDa7a4UMtAI4NdntT1D6Gb1NZNSyfWcAPCTjZqNqult/fm2Cp0kW+6DQ2xBDcMsP384vfrBfen+hy5L9w27NL38XmzmVv2sjpaGAj9BePPTIemWwWen4WOuzsLo3DNv3JFuvefc9NL792RGYlStfLkyBGFCwO/+tz97IKd2zhxwSLrw8mPTJVcdn8658PB06bUn5HSutK2ZjoMGn5XHsp99845szOK9ftu978ob+6WnX7stM7QGaPemqwf2S3cNvSDX27XxXnWN+sZz4lx90Y4q4M/3fzYsDSk8t1sHn5t3x8vtKd0bz/7yu2u8qj+uuB/PGAGGsV7w6q+vekYci3eM79oan7oH+CmA9bAmvOjfi688tijH5bXYlsncP+yydNtdAzLwU0hjXYBfqve6m09JL759dz5e1Y5GFs/uKPBnPfj3g+muQlbPqJPji4p22oQpZLD8rPr/a8faOl47ppBxDqIPU0mT6sfyedfry9c/GpJeendwoauD0+XXn5THFEOmyteW39HWufEV144P+PGHDj085prMn/6FPRg68vIs6y+/e2+el/Dg6CvzdW3pUNWxOF7/f/11fud2f/Zgeq6wX7Ej2jhet/3s+ueUC753Bfi9Vz8ZErvoimPTMSfvkfvUypjQxap319etfK49pavA7534aQjz2ptOzSn6wI3yNfW/a/yu/T/uWHX76o/H7/rjHSlka3zAn59d6PGgO88q2nVKerVk06Pu5WvLv2XiDC/DnCdfvSWvtLGniOG0GvjXnAKOkt9xb9xf36b2HlPwviHAz3sB0j5Pu+KqS6T5Fp4jPfbsoOLKJwvlPjunLYzX8miAoGttBVoGqvYAfxbuQnHMkrTM4+e/+Eme1GBW6FLLLZpGPnN9Xpa24x4b5pnrW++8XnrixVuz0NYibR+DeDIdedKuaYqpf5puuePs4v/nilLbvWvF1ZZMu+6zca6ne9QP42QYMJ+BF4V4lmNVwt8W8OPTu58MS//YaJW00G/mTmNfuOlL7fN8z3YtECdk3l87Vkvp1o5HnWrp2dq5WhrMPa4jfK7xG8/8H3V2rQjZO13vmfF857TJPX7XjtWe53rnugv41Y0jZ6KWCXlmtprkqK832nKNvLnPan/9Q1GPgenw43fK69elrK0SmGm2X2ZvWV9HG7Qx2tHIgs8dBX58e+dfw/IMchPMJp/yp1/I8W9//+t038OX5rrX+qaWaiZT0X+OMQ7kZ1z/ffl47f9aSttqkAEXHZmm/OXPC8N9RPF/Tb7jPhHLc6/dmVdR2Mzp6cLx8GnqA4/YLstKPC94SQbJQrzDsZDNqvbWF/wh21XA76+22nfD5DyTuqaY+mf572nnHJQeeuzqNO+Cs+eVHIwipxf/9a22Rps8w3volbo5xtgq5XqP0/Fxwx348fanD2Y7wh5YJhXXj+OBD2rVhg+jn2p6E3r2SOZbud141hXg15bRz96Yll9p8Tzhbc55Zs7L3zbYZLX03Kt3ZrsV9VPwRB3Vz73Rj1V2a3ylq8Bf65tH8gx+S3htVGbZck1ea5lL9dX3Nbmq2S19WG5PrY9q/Nbnnh022fNrMlhz9mt9MO44Gauv14SKuo0P+D37/hGXZB3++RQ/yXO0vMv71b083KjedDN+FyiaTj/74DxJ8cqbTk77HbZ1Wnn1ZfK8LM+Iuocc12NBrR/Jbo23cUwfu75sw53Xlqg3PW0Y8KvgC2/elf5YGAzLMTbZcq30wScj0y33nZsWXHSu9MhT16fnXr4jb0+71fbr5J2JTOgIY9Ee4Ncgk5see25gemD4FemPKy2Wvv3tb6aLrj4uPfrooPTg2CvTHvtvlq4V8b15dzroqO3zZK57h1+cDj9mp7TfwVunI4/bNW1TOASWjl1/02npyuv6p/0P3iZPMLHN7XGFw+D+g4/YPn82EnOt8TYBbfSzA9NBxfFNt/xrnmBhkly9sWsL+D3nvY8fyt/8N6tTG1znHp00rIhg9j1oq7TBxn8p3r1DntxiYtZRJ+yWTjp937T9zuungw4v6vR5luKG205LW223Tt6NzTW+dX3r/QPSEcftksa8OChHjYcWvPYNAJPD+p91QNpo0zWK5/wjH6MIsiGHHLVT2mbH9fLmOEefsGueiDL2pUFp/0O2SZtv/becSXnns1qKlVJ2F/Dr6zc/G5pGPz4w3XLbOWmW2aZPs84xfbr5rrPTk0/dnmda29FqwKVH5NntlvpcdPWxhUPwt7wT4tBhl6cXXx+cwcts14uuPCa9/a+aYal6X2dLTck6Bvwhx5y/YcOvyrt4/d/X/y+de+mR6ZFHBqYHx1yZNykhr7vuvXGeBHTZdSemzbZaO22+zd+KKO+k/IlSsn1s0W+HHrNj2mLbv6XrizrYuldWac/9Nk8bb75mOqbg01vvP5TOOe+IvDuabyM888rtWb7J2n5FPz/65I1F/9eWUlkpYQLl5tuunXdVBK6PFmCz5/5bpPU3Xi2vfnjjoyF54t/hx+6cNwDaavu/Z71+8pVbsmxWtblctJ8+VAE/XRlegPvMs02b96MwjHX9raem3y4xX9p6+3XzxEygB/x223PTtNd+W+RVD28WBtrqGHZni4JHV9/UP7376UM5Y3TwkTukXffcuNDHffMkyV332iRtuMnq6eiTdk/P5YzgiHRVcR3+mlB115Dz0yXXHJeX9VkOeO2gU/MWvyecsnfaZPO1Cl3aP79PRuLw43ZO+xR6ayOWIY9ens447+C06VZ/TTvutmFehvbeZzUnXNvIXmeB3zMY8IuuOSb306p/WSZ/Snz73dZPq621bBr9zMDC/t6dDi5s3maFfTJJ2D3X33562nmPjfOkXhNabZF7yz1nZ7sV9ZpQaQTwAyFr1r8/2XcK3myUtttxg8KmHpG3oh109xl5s6nnX78rjSkCRisvrEg67ZwD0omn7pV2KfqOHl97yylpr0IO9Z+JmB//e1RuF9kky4ccVcjgS7em5wscsmLFltQ777FR2mPfzdOwx67K/VxVv7bK+IAfiOOJZbA2GrOBlqXWGbMKp/HMCw5OZ55/cHr742Hp1nsHpMMK/DF5zy6QBx25fdppt03S6mv9MW8idUnhMJxyzv553xYRv307dt93s7TltuvkSc90yvuPKGStX9Emx486cde8+okNtnHXRoWub1fYdpMe6dCjBVbtW9gPMm0iILkLrPK3ocD/7Gt35F3TLC+wVMwyhTuHnZ+WK5wBsxYtR1rzb8sXoH9o+v3SCxcgul3eQ54R8IwJAX8UUYiIyLaFNjEYXUR9Kb1cGN9T03e+++3MHI23kYe9yo8v6mH9I4DYdqd/5PXr1inbztSuR3k3uwJkbXCzeaH4Z1x0aN5J6bzCkDzw6JXpl9P9IkeU1sCLyuwD/tMiAj2+MAT/+veX5y9MCPhXXeMP2XBJvbqOALmfgZ9/kTnzZgzWcNpy1oYsBGO+heZMf1r199lZMYP/rgcvLJ4xVVpo0V+l1dZcLl8D8ERBPyv4YQbzQ4VCWENqc5ub7j4rL/kyG15b5i8MKidm653WyxHDGmuvkDfR8F7DI9o5/YxTp8V/v0De8czGN6Kg7gR+BRgYx3zr/Qfz5jKLLj5vntSY0otpk23+mub81Uy17M2UPysi5snSyWfum506SyI5Nza8maZo7xJLL5iXDZ5QGBHetCGBqvd1pjCeHQX+KORb5LPlDuvkDYqGP3FtluNb7j8n7yZpjwK7K4rCLUG09Mx+D5by3HLf2XmL3W995xu5vywbsiTILPXfL7NwXrdux7MFF507f6/cUAL5sQWq7ZwtCbWkyG6W9km3tJHM/2reWfN2qTPPOl1eK/zk87fkbzjIsC2/0hJZh3YpZNUSOftGzF/IpuPf+Ob/pX2LqIVMk/Wq9kbBnyrgZwNE8Geed2je3OS4U/Yq/n8q99nrn96fPvpoVF7uKWrUvtXXXj6vZFl3g5XTnUMvSIstuUCeCIUP9qmwuoUt0i7yvdPuG+V9DixvXPqPi+a2H3/a3tnRkVGg15aR2rpaAEE3LLk6++LD8nLRXxRytvRyi2QdEwCcd/lR6Sc//WHeJ8JOkjsWdoUOLVU82/JIOvnSW4Oz4WYXugL8imzFo8/ckOXAOy1BtCTWMs5X37/3870aJk+/X2qhbJ9OOnWfdN9Dl6TZCpn5XeE4mQi8QNG+Bx+5ol0OWpRGAT8n5X/+57/zjqmWINPNWwtZ3+/wrXM2auSjN6Rb7jovTT7VT/MsdvrNTv95taWzYy+yXmm1JfPeCrJTb7w9JDsyNiHjCH79G19Lexb/PzT26rwhlb0vVl1j2byx2Vp//1P+mNeEZLNcxgf8MtZjX7g5zTH3TIUcLpu2KPravhp3DrkgffDpiLTkcgunpZZdOL359rAiYNshO5BW1+CBttgxlh7bZ8XH0WxAxp77ENnvl14o6/NiS8yfpiraf+3NpxYyc3ThiH4nX7PcCr9LXy/0jSM64JIjc7ZhqWUWyTK+QiH/DxZ19T2XX04/ZV7+R1ZOHXBg4SjVtjJuOPBLFzImvpJmaRCP3XIEHqnNM2w0cPtd5xdPeCudeMo+WYlEHzXFaD/wq7z3rbdRbR34sLFXFc98Ol19c78iqvlaOrmIkKXvGaU1C2U4ut/uhYGYLJ3Yf5/06aePp2OK/+2Wx3OWRn76xdvy0r7pCkDesPAcn3vlzry22YYpAN9GH8ecskfeNGKhRX6Vdim8SOvj191w5fxhkHI92wP8PsbDK2XUACpP/qpBJ+e1mtJ23ymM3l/XWyFdcNUx6etFexjA596+M4PxOhuslNNCP538h2nw/Zell197MM0+9wxpvcIAOM4QDn744mwgfvjD72cFMobEsG+9w7qFsM2Ylena207JDojlgCm9lo44dpcc4Wy36/ppysLYETpRCyWzVzuQ10/dCfwKXr7w6t1Z4Bf67a/yh4v0tT38CfY9Iy7OcsUYAohtCq/XR22seJi2OO++nYr+IvxrrvPHLK8dUf4Jla4Av+tyJLT1Wlm27h0pxf90jnYYSKD10SeP5CU8vk+w9Y7rpYWLiNwQl8hvtbX+UCj7zOnF1+5JBx+6Q/puAcTX335a3hSEM2SPBjudPTjyynT+JQzHdzOI3fPQxWnnPTdKGxfRseWQdHbQ4DOzLnAsyQsZ8fGP/gMOKKKZr6azzjusqNvL6W//WLF49k/TIUVUNtkPv5tO6r9/GvvkHXkvBt9fsGxwQqAyfuB/PG849c3CobEdqcmcsg5PvHZzev75O9N9wy7JcquNr781tDCCi6V5CgAc9ex1mUcc+4V/86vsiNxy3zl5lz4yfNfQ89O7Hz2UDSjnUDusj2aIOVCcRUs7ny+iqDuGnFdEiFcWz501O4+WhNK9X043ZXYI5px75rTqmsumw4solcNpm9+PPhlZPPuYNNUvf5E3wQJENml58V3j2TU96SrwA6IXXr0rB1HHF6D+t7+vlrcatinM8aftlUF/vgXmLOzTJrmPbGfLkTr7gsPz1tZf/epXcmbQkGd7ZVRpFPBvVTi4doy8Z9jF6fpbTysA8If5uxo20gKaY568Ld113yXZadl5r3/mrM9aa6+Yni36XVDGAX3h3bsKfV+kcGDmTi+9fk+68Y4zss2SjbIp1z82WTXd/8ilOUDbeIu10osvP1A4PfOnXxdY9NJ793TI4Rkf8OPr8f32zN9UgW/kDJ6QPx9/At5LFfj24cej02FH7ZIdyEuuOz7b/lUKDEjp1XTYkTulbxZBm03lODLkmMz/d6H7wH3HXQtnc4qfZrt+2vkHFfyaLAP9iFHXZ3tmjw6brnnms8/flZ57/Y78LZpLbzghO7v031AVJzg7Su8Nye1vQsR/Z941yydUReS777dZNkYqoINtqnLNjacU515PBx62Xd6cgbNA8TsD/EDQ2KjUKINp45H/+X//nfY/aJuchgtjdORJBeMLRbj+5tPUPh16/E7p/xXXiQQB+AGF4j7+ys15k4+/Fo4CY2NpnB3dpKY2K6LL2x8YkNvCq+Jl2jLxwCO3TW9+/PmndT+v24SAf5W/LFMY1Z+l2+4ZkB57/OY05omb0iNP3JB+t+T8OZVtr2bRny1Fz7/yqBzND7j4yGwwbLqiPXYi++73v5VOO+vQNOjW8wpj8/P0z81Xzx9boVgXFwJ22fUnpG984+tp74O2SAcdtV2ORAwXLLbEAhkUBhaGnnOz6O9+nYaPvDltuU2hlAUvzZ8Q5S9YKNYe+26W+/OQY3esfQ2uaOfEAH4fkpl/wTnT/IVn/8JbHKan84cuePT3Fh6yDYsWLWTsjXeH5I8WTTv9VOnYU/bMO9XN8+tZ0x77bZ4jZhkRXj+FrnpXZ0ojgP+fW6xRRM/fLpyYS2pyfPupeVdDW8Vyyk46dd8ctdjJzPpeRuTia4/NXyO0PSk9Oeq43fIzOHRS9Bts8pf0lzWWyw7E+puulk4pPH7bnNLNyDpxCjl5K666ZP6i2PSFsdT/Dz91bX6Hoa+zLzksfaUA/kMO3yk9/9LwIqJYIusVGeR8XnzF8cXxe/NGNxzldz+rjatXtTdKW8DvHCf4/hGXFdHgFNluPDzyunT3vRfmrNff1lsx3T5Uqn+anHWwWcsqqy2Tt5k+8sRds+xvsuVf059XXTrL/q0F8PuwFQfRpMVHHr8xR1I+PiTD9qMCtDk3G27+lyzzJljdfOc5RYS2Xc6S2VjGTpI+hjVD8U7g5IMwdobc+8AtiwBij2wfat/ZeCLddv95eYiEUzJd4SiIQGUhzanQtq4AP5nVzyeesW/OeJxx7qHpxVceys48wLGj5RSFs8Yp2XO/LfI+9vsftk364LMRab+Dt8mR5Ve/+tV00JHbpQ/+VftUd9V7qkojgJ9TLtX/85//OI0ee2O6efA52SE748KDs/2VPbrljvPTKacflL7xLTt6bpyBf7Mt1k6vvjEk/bro/5WLIIXcLL3commR38yTHnny+kJG5s2ZAR9c+973v10A3GrpvlGX5Q3Kdi/0/t33Rqall100fx2PDDQC+PXDi28PzhuGySTbGIxDbpfBmYo63z38ovSnlZfIWDHqsZvSBhutkQNUY/l5k6wC9559YVjabocNMl5dfuNJWbdztq3QXY62jLjvGCxT4Mw5hbN+6rkHZhm96a4z09inbs7ZEh/aWmvd5XMW7p77L8kf7PGtAx/w+r8iYJTd2bvAMTb84MIpNjylDU0A/jvS8isvXghnbd/5V965N6capNOBs9ShVPa+RWWW+eNvUr/CuzEOlxncAeBnOLyP8Sc8PiqSPyf69PW5sVNPO0Xes5tTgDlHnbx7Bolrb6GEz6QDCyD84U++n26565wCDDfIaRiTnuacp4jy/7G8FqZHnxiYPyRDqezz/u5Hw7PXNNuc06flC6USaZ1z6REZ1MuGvi3gNxno3U8eSuv8Y6XsjZm4ZI0mgTQXAT8Yl78V56WCpLQYFFHVORcdnlcBUAT7NHMUGB9b1C5beIbf+c63snG/4Y7T8nOt/1yicLKA/X6F8gPu7xWgYAxKFsTz73jwvHTauQelOeaaMfFM5yqE9LuF4px3xZHZk5RSto/5PPPPnsfOGWTKPlGAX8RfRHEM9gtv1yJ+7RXR29BI9A8o9z10qwz8lOL2e8/LRhEgLb/SYnmfg/MuOzI/r+o9nS36vivA/8nnEb8tdDkx2nb9nadlQ6jfDGvYsVBa2pDZEn9YKM8ROeXc/fOwGmfahzmOOm7XPDRweRE92GlvvgXmSKutvmwG8EOO2iGdfdFhWVd813/1tf+YI4m/b/jn4vxPsyECqIbn7BpJHgHvDkUk9eKrg9Ma66yQeb3gb+bKKXGzyS8tnEv7i1uR8OwLdxVyN0Nap3iezx53Bfidc//xp+xV6OXPszNueAronnn+oWnY6KtyytfXKNmOVVf/Qx7OyFk9elM4MYY6DJXgxT+3XCN/4EhGb+TY69KshU7TERE/fonUbrz99BxRSg+bSOh5gwvj7SuOdMj4s/FSgMvxcX+/wkaZk0E/zyqCGuBOn2afa4b8ISjfjbDPhA1aOHfa1hXgV9jDe4ZfkmWZvLB1UsFS5w+MvCLP1Zll9sI+rbRE3t3t/KuOThcVDqIhP2PAhjbx6PzLjyqe1f7lYY0Cfk4ReRz12PXZsbKP/dmXHJ6uuemU3BcA1PCseVsCFkM2m2/5t/TSq/ekBRadq+iPpfMGNYIue3mMeur6fL2IVmbGUJbhjtuGnpf7WeD51jsPpWWW+00eRn3pvcFZV6vqWFXaAn7ZZJH3dwp7uXPhQL765n1F0DE0z3f5v8LZ26ewQzCGIyKrDTNk6YaMvjyPzRueXnzpBdLsv5oxX2NvEthiKOaBMVek1dZeNvffssv/Lju8tU3OjshR/sA7zkiPFdhkt0Jb+HL0RfxzFjKJn7Iiw0ZdmR0Jtv2PBVZxYC+8mp7VlqZ2CfiPO+HI9NrbD+QO9cA8c7BQervMGT92nDKMffamvGTBy958b2geNz/ixJ3TTXecnY0wsHe/DpHme+iRa1P/wrt58TWRXW0jmpiNO67UNiwxnmny2esf3p/f59iwR65Mx5yye7qoMEj2urck6dGnb0hXXH9SXgbjwyUjHr82XXz18em1d+5PL74xOF14xTHpvpGX5vSTvcs//GxUunXwgMxQ48qvvHVvUZen0mvv3lfU/6hsGLXprQ+1v1b/qFutHWPSs4Xj0v+cw9OjY27J/8fsaGmusy84NJ102r55jMbkLEuVRo65Lg+HmPR4TaFgg+48M6frLyva98SLt2SQvXpg/3Tf0EsKT/eGHOFvWAiLzAllIjif/HtUuvOeC/LEt8uvOTFdWbRZtOLbz+cXfD+m/+5Zea+85uT0+PM35YlI9mEXCRtzYiDs2PbWew+nARcfkdtpfOmdj2srBDgwb/1rSBFd7ZWefPmugpcxW7h5pSZXw7M3q0ijGR6596GL05U3npze/viB/Oni40/fKyuQaPHyG05KbxZ98+b7hbxd9nl/3dgvvV0osec1qs6154zMRnHgnWcVPD4uy4Nj7XtHbZa1yXuXXHN8XpalbZYmXnzVsWnEk9dleX27aItxaF/Tu+Lak9Ll152Yho+9Ok8eG3TXWVmPHi1kwoQ0O8499cKt6dRzDkwHHb1dlp93PxqWnnn19rxjmI9SPfH8zTnyN1Hvmhv650lTr7x/X05xkxE6bLmrSYJ08vVCfs4rDN3Bx2yfBt56RtHG0Xlikfc9Vcj5u58Mz8uZRMw13axtNlRVgmfk6fZC1s4ecFwyGz3OKc55x71DL86RvK+V3fcgp2hsevODIXmXMg6Adt9x33lp0B1nptffuz+39eh+uxV6f3K67JoT0ujnBmb9N9HPKgq8HvLwZTkFb5UDXmormXhwxBX5Q1h08rGnzBkam4YUsmR1kN0vbZUsW8mgM/qyS0+/cnue5GuSZk6fF3piIqDoyoTcRx+/Ib9TPWvtqs38v/jaE9PV18s+PtFOORnHN7x66vlbspOx/xHbZFtii22BFnm/6PJjCnnfLn9yGWiZtHx24TC9+eHQ9PJbg3Pd7fCmTjWb9J/vqi81m/ZEzpheWMgQXoTdrrr+P0tNzu8vnCB2WF1MRLRz6BMvFLbts8JxLuwM2WP7rrqhX169Qab0F8eWc3b7fQPy824ZfE62jx8W940o7KYJ2Wedf0i66rp+2aF49o3bC2eif97y15bTtxbX+xwuZ1O7/7N+/1m0zbUffPpw0Zf7p+GPX5MxxnH6bu4XZ1p6HW8U38m/4LKj81AMXp9f2B778N9w02m1dn9SRNzF8y4peHjcKXtkW8wuPfvabdmemVRuJQBMOmPAwemgQo4G3la0898j0pMv3ZKxzgR6/WoZJ1sHW+578OJ0+PE752GCJ569Odfl1bfvLQKRI7K84qm9T6LPtOOdoi7nXnx8uuXm24rrv0xtAv9LL72cDjviwHTnfRflGbgPFN6Qj8Y8UHg0jz53fQE2Vxf/X5rLqKeuTWNeHJgeLM6NKIzVmBcGptEv3lBEjDcWnXtl7b5HL8/PGDX2+gKs+heCu2P+PvuIMdfm47XzrhtXPPuRZ69LY18amIY95v2X5nc8+sz1aeyrg/I7H3/ppjTqmesKA1q898VBhbG8Mg0prhvx5DV55vqwsVekh4u6+j3iqWvSYy/cmJ4pOqHfOfvkaEOqbr/DtsyzLE2YGFF0/uPFc8a+PDCNLZ4/fMxV+b1frtsVGcRvLoDgoCN3TdcNPCtHGjUeXZ7r/MQrN6cnXq0V46l49EhRzzFFWxTvGP38DV/U7eHHr8rP1gbbM1503TFp5tl/meceGE809nPocTvkr+6Neb54xstFHYv6Rbsexvfi/zHF/2OLZzz18i3poUJwN95qjfSTn/8gZx6Mqy2xzAJ5H4bRheFzr3bqr1o7rygUqbZ1654HbFso3oD08Jha31f1T6NK5u/oy4q+uSFvbem3Psz8Ktqk7/Fv7CuD0iOF7I16mrwNSg8WnvOIx2v8G9cO8lbfX10pl+f15A+PvqaIRg8rnLh90/BHrsrH2ssT9SGj6jmsqLO2PVTI6diiD/XdkEcvyfwfU8hmrV+Lkvv16vTos9dnntAh/zv+UNHGUU8W/CiuHfPyjfn6h8delYY/cVU+/3DBE+fH4Ekha08U8jS6eLZteekuPo56pqaz/qrfSHLv3fSq4CO9zXUsnue5DzymfwbmbZ/VpabTbRU8I0tX5Yj5yKP3SSNGF/pVHAueuV+/jnnuxvR4UR/Fb9fk40WbbDvsOnVmS7RR3bRLm+m+uoWcsA3ZPhQ808awD/TRc0cXTrbj2vnIU/S10FW2pPh/ZMGHWh94fq0PxvH0pqynud8KHkYd6NqjT19f2A1bCNfarc1DR12eTiic/lPPOCQ98vj1xfH260/w9dGifrk/XinaUPwdVTiIzmV5Jx+ZBzW9JUPsjfqOeEI/3lS055p8fTxv/EXdrshL8E47+6ACsPbJYKwt7a177V01Oc998VjBi8/lh23LfVr0If6PLfiHpyM/t8dksKb/N+YVR57n72OFfdSfjzxdPFN7yXJxn/717MeK93iGe9lS9/vdvjYrl+eVVkMLh/CAw3ZIVxaB3vDR47COvj3+yri+j/7P/VG0s9YXn8tLblNhk4p2ZjkqjpOr3Ff6qagvOaVDMHRUEay5fkzBD7KNP/jkmLbhn/u8h3w9VvAjy4N3F7KhfnQ2nvFl23d5xux7hl2Yju9/ULrzDgH2l6lN4H/11VfTHnvukU7uf3w6/8Kz04DzzyrKGZXl3Irf511wZv5dPucZF158bjrqmMPTNttulU7qf1y64KJz83Hnz7/wrHTRJefmct4F4953wcXn5GPqMe6ZZ6YBxTuc865x7yiec9HZucT/X66D552djj/pmLTD9jumgw46IB/z7nJ945n199bKWcUzzk0nFM/Yepst0jHHHpnbNSEeKeN/7ufFNUU59rij0k477Zy2326HdPgRhxR1PDOd93k9nY9nuieO4Uvtd/Gu4tozzj4l7bvvPmnbbbZPu+2+ezrtjJPThXhTnHffl+tzVub7OeeenjbbfNN00snHFv1zTj5ea3PRD5cOqJWiPy74nMd4V+Z3PNf1tTqNO96Z8kU7K55Vq3c7+VpX1F/dq86NKwXPc/vPTPsfsE/afc9d0rnnFceyfHa9bVHK7ZhQG5zP1+jjNtpdO1+cq3re5/dE+eJ5RYn3/8c9HSqf86coBx68b9pu+23y/8Ezepz1PMtSSd+Ld+uTaFOU0Of21DGO1/Pli+sdr7+/7tq2eBrHlHxN/XNyu89OZ597WmE7d01777tHurBo2/jsQlX54pl1/Vt+V1Udy+c7Xmq2Wb13233n3AZt6Wjd2yrj6tUx+Yprx9cnnS81OdXWrbbePB19zGH5/1qba+9Tyu+s/l1r038c/w/5GFfimHa1dU2UOF/jwbjr457/5E3RrkLH2Pp99tsr3X23odMvU5vA/+KLL6bjjjsuPffc8+n99z5oaBk69IF05hlnpaeeevqLYx99+HH68IOP0rPPPFe884X04Ycf5fLJx5+m1197Iz3zzLPp3XffTx9/9Em+/oP3P0yffPKv9NZbb6d33n73i+e4/v33P0hvvvHWF8fqy3vvvV+876P02Wef5eL3exXXjbcU73j88SfSGaefkR56aHj1NV0oH3zwYfrXv/71RR0V/OhoPT/++JP073//O9/v7yef1PhTdW2Ud955Nx1zzLHp8bFPfHHsvYL3+kH/PPnEU7X+eOe9zO+33nz7P/j91lvvpJdefLl41ntfOj6pFDLwRlHnt4t6Vp2vL/h+86Bb0gXnX5jbXXVNq/xnGXz3PemMM8780jE6+/zzL6annnw6l9defT19Wugy/X799Te/pM/Kq8X5V1997UvHJuVCf6644spcJqRrk1q59trr0qWXXlboxpuV53tj0dZ+J/dLI0aMrDzfUwtdOr+wV7fe6vPdX6bxjPG/kE444YT09ttvf36kcTRq1Kh09tln56xC0EcffZTOO++8tM0226Rtt902XXXVVRmonnvuuSJi3TdttdVW6ZRTTinA78PP70jpwQcfTHvvvXd6+umnPz+SCuPxbjr66KPTxRdf/PmR5hHn6KyzzkqPPWYf595Dn3zySe77l1566fMjKb355pvpkEMOSeuuu27ui8022ywdccQR+Zqrr746XXjhhZ9fWaMnn3wy9evXv1CqNz4/MunRGWeckW6+2XhZ++j2229Pl1xiHLpF7aUhQ4akAQMGfP5fjS699NIsR5tuummWox133LFwnh8qnIHn07HHHpvGjrVH/Ti67rrr0jXXXPP5f5M+sVt04vrrr//8SM+hQYMGFQ7LFenTTz/9/EjvJ2097bTT0pgxdonsPfTxxx+niy66KN1yizloX6bxAv/xxx/fFMM9YsSIdOaZZ34JWG666aa08MILZyNx0kknpcUXXzzdeOON2RH4xz/+kY//4Q9/KMCkX76eUi244ILpZz/7WTYa6JlnnsmG5Fvf+lbadddd87FmEqcEeDz6qEmOvYc4V7I9DHEQpdAnm2yySe4X/fG73/0u7bTTTmmPPfZI6623Xho6dGjuC9mFZ599Np1++unZuXvrrbcyr+69997c984jgnn//fene+655wuHjtPBoQPI5fc//PDD6bbbbvuSzJTpiSeeyALu+ej9999PL7/8cnYyBw8enOuAZFGAkfTXKqusko455ph8vD3Ec6ZIUf8WTZj0+TnnnPP5fzXaYost0tJLL50dACCz8cYbpz/+8Y9ZrlZcccWs42SCY42A0Q033JA++OCDfGzkyJG5T9955518HpEv/fP44z5qU6OnnnoqyxHZCSJTjtFZAN0MAiRXXnllET3brKlnkT647LLLciDWV0hbBZW9zY7TlwsuuGDSBv5HHnnki5QEkFlqqaXSkUcemQ3CXXfVJieI5FdfffUMDhokUlhyySUzUKAHHnggZwBWXXXVtOeee+ZjzaS+BPyyGiussEI699xz8/+UhUOmP/B8zjnnTJtvvvkX1zDOu+++e87GcOT0SZyXHWC0Dz300Jw94NzJJnAWDj/88OxEcODWWWed/BzXb7TRRmnLLbfMfwPcgy6//PIvMhHqw3BxKFZbbbW04YYbpjXXXDNnkYDGiSeemNZYY420wQYbpOmmmy47J+2lFvB3nKqAf+utt879EXzUv/POO28GSnrPKdBvf/vb37JdOP/887NM3XfffenPf/5zdj45bZxOmahhw4Zledl+++2zjAkivNcx/5MJ/cYBcO8uu+ySn+1YM6gF/D2LWsBfou4G/iAAw+vfbrvtste/3HLLZcVGhgeWX375HM0hKUH/U/IyifYpNxIB3HnnndlolCOERlBfAn6pexmXhRZaKAPwWmutlUFcJLbPPvvkbADDyuECrIZanNfXrl9mmWXS6NGj0w477JCB3RDJ3HPPnQGfQzfTTDNlUAbEp556albEKaecMv8F9rILhh/Uy3PKJHLkJDLkv/3tb/PzRZJzzDFHBhVytMACC+T3/P73v8+K4JiM0cknn/z5UyZMLeDvOFUBP93U3xzH9ddfP8uGvhk+fHhaZJFFsjyRmyWWWCL3P8dyr732yoA066yzpoEDB+bUP1kUvetvsqlvVl555SyLhmSkb/W/5wB/Q4nTTDNNdi5lejgIzaAW8PcsagF/iSYG8ANoQM5z58k7T4kD2EWOK6200heGV1qZwgP1IJ0ogoxUf//+/dOf/vSn9Pe///0/AKOr1JeAnwO17LLL5nFZBs0YJn4ycsAezxHAFX0bCuAA6GuGnuFGIjdRl4gP8DLqIn9RoL7af//9c+r3oIMOSnPNNVd2AgCC+z3Ps6X8y8TAAxHP0tccBY4H50QfIcelmMlXkLaQ8fZSC/g7TlXAT7+BPaMEwDlhMcSjn8JQifqN+R9wwAHpwAMPzIAki+NaQ0iifwAP2GV1gLkswG677ZaHC8gUuZFt+stf/pKHAjmXsj3uZeybAXAt4O9Z1AL+EnU38APv2WabLSs2QyDF+9prr+WUHGAR9UdaOYih4Bi4PghoMfCMCzLOq0MNH8Q4cqOoLwE/kJeGjVR/ECMM2KVVkahq7bXXzsaecQXahmQiA8Mgi/Iomr4TkYnapGQ5a/PMM08GcpmEGWaYIWcEOAYic84GZyGehWRxfvOb3+Q0v3F7wAEEOB5kydi/DBGgkRXgKJo4aoyYY9EC/uZSFfDHpL56MjzDuYxJceQImNN5WQCZHf1rwrHxe31JTjyPQ8hGiObJ21//+tcM/Ib/AL/nup9jYI6HZxtSYOcaTS3g71nUAv4SdSfwm2TDODDqlJThprgUWWeI5oCI9D8gD5Lqlzo2DhgEiKSEAUyzqS8Bv/F3RrN+pjIjB6hjkhwQF61Lo4q2AC+lErkjEZ4ITsSmj/Qzh05Ex9nTx/4H7sb4ORqyCAx1GHPORBAQJqfkBviL9j2L4fUeckwBOIIcRfMBpINlgPwFHO2lFvB3nKqAXxQvtV8PLpxL/c4BQAcffHDuL8NChvnM9REEvPfee9l2AHi2xDAUx5F8KMCLzPjtuKEm15p8yuFgX8iWbAH5bTS1gL9nUQv4S9TdET/ANssb6FiSp4RSOgbwqwyuWdr1s3Nd1x3GuS8BPx7HvgL1VOa36/yO6+P/8nnHg8jXK6+88sV5fa6v9X/5WpE9eWnLUL/++us5QxTvirp6Biq/0zCSUj7fHmoBf8epCvijj+op+jv6JK6LUpYHVL6W/TB5szyPRx+TCdfFfWwM20a+mkUt4O9Z1AL+EnU38PdE6kvA36IW8HeGqoC/t1ML+HsWtYC/RC3gnzC1gL9vUQv4O04t4O9Z1AL+3kMNB/6YHWuCTWeoCvgZUzt0GeM1lmysGBkLNmHHrHHrecupPmvEAZT6IH/NCt95553zmJ5lP82kKuDXDmOHJhgaX1TMTtYBbaWpq8g4tolylsgZQ5eyrCfpbXzEz0ZSFfBLjRqnN+Ne28zMN7/C2P+EyEQ6k+16OlUBv7ZZoRAT1ownM/rmOEyqu/zpy8MOOyz3YcineRk2OVJ3ht/8C22rJxMjO7IjXSOB3xwNz5KuDzIsZPJfuS3mD0jzT4jopJVEjaa2gJ/9srcF2xpkXgt7V16ZVCZ2wByH8i6nzaQq4DcZEjCWh1HYpmbb1+6i7gB+WGXOClyCT92xq2NDgf+OO+7Im+ZYY23GbGeoCvhth2rnPkv2zOK2fp/hodQmdjluKRblQephTfbkk0/+xc5cnmltr05kuGKSULOoCviBpnXFZpEDCfU1MYliEzCgYVZyPZAzuo6Fc+BeM5gpvs4LQ8YAxL2W2LkGqHpm2SB2haqAn1NlFrUJU4y/d6pjyAcwYYTDMdMO7VVvE//MsPe7GVtAdxdVAb9VB/YIIJ9WKOhvekE+GX5j0K7HH30XY9IIn41Ll/vNMbpX5pN78LZeFz3X9WVnmLLHO7yvynHXr/ZQ4LipM/k06VJ/WSFj9juwia1zPd9zFeAKqNTbJLsJUVvA71naGWCiLd6jPdpqzD4o5IjjaUJmGYBMAJ5lllnSfvvtl/tGW4BX8MH8kTIv8Vq9HbNiiK3hQHtvo6gt4Pe+r371q9k+BXEEvvKVr2R+I/XD26iz/rEfRixFjomN5T5vJFUBv8DDCoiy82GprImW6hs2y1/9puhPNi12zCT/9bLoHWVZ1yZFG8vzfppN6tEW8KsbfSnLozqSUfX0O/TN/2UbWCbPX2yxxbItxE96EWTSanllWqOIDjQE+D2I8v3zn//Ma2fb8lInRFXAD7wjQrKJj+fbCz628kRma3MCKCnmqYeNXWLLXjO+LRFzTsRV7qxmUBXw45F1wupiGRlhUAixLIY6W85mPwKZDEJiBrJ1xpYkxS5jjlm3DkB4ijxGQsN4uI5jYzazZUp4BZCtfrBFbVcJf+uB3/s5GergfAg9sszOu9XLTH79qm3qpq2W6c0888z5Gu0DBN2l1I2kKuAXKesnjpH+1v9IREQ2yKaVBvhgZYrvTgTAkgU8keXixIloZQ70MXnAVyBndYJjZB9AhKPgOWbI01Nk1rqIgjxyRLzT8+2PUNYFcmujHGCvzoo2MXLaw6ib9e5dDDADbymn9fGeN//88+f6hBM4PqoCfoZIhO4ZHEJb8rI3dEadHZM9ccyqkJAtTr1z6huEZ1YDsUXRFoaYc4zXVm94JhC2+sfqDwEEnskOaItn423sENpVagv49bclqlYVIPprB8If/OAHGWzJiv7He/UUFZIBv/HB/hVkSWChnzubcR0fVQE/ZwpoCcTIubqot+wqGQlHRsZWtoWOlOWYUyZLKJgLWcBr2cO4xr2yH9oX+yyQtwh4mklVwM+22UwMr+kDGdRfHBn6wI7pJ8fxg50mT2RJRF/fN9rKebLiDDbRN3JC5zjgtj3ngJaDgK5Sw4CfQnkYkAZcnVWU8Y3x85gwU+erMOCv37lPHRTHecMMHmJAGQZMBIa8+fBGm0FVwK/jGDVAR2CAgjoxgKJkW9tyAABD7FfuPOPsOYwb8KTwhIgzxMDiheu1ER8YdlGArAeD6Teg5Rx1laqA32/Abzc8RlKdtUFUuOiii2ZlYOz8tuaafGg7g8xZsD5fJMxQqHMzPNxmUxXwA15b/2ovA6YvyDWwZgAYNPtTyAQAOFktgCByZeT0K1C1FJEscfIAMsOCz9KsZAJ/GScOH6cL0UfHZGIYEEDHwEbmyzuPOuqoHN2TpyD1s0Wu/Q/0kaJunhWZNvWS2WOo1Y8scv5iF0bZNEEAPSvLST1VAb/3AAO8sUsn4yqzpU4yKH4DGvzwPksxgQLD6vpwrhBbYhdAMhXGWN1s4EQf8IBOcHTYs+mnnz6/3xAVp4Acs2fq49ki065SW8DP+GsDPeKgW6oI+CKT6X/10P9kSf1iQyx7VLCFeOHZ2ktmGk1VwM8GTTHFFJmfZAXok3nDRcCZTUJsmPaRQ1kw/Utu9Q85kL0l98CT7WAntNUzybrf9tZgI2SAOWVVoNVoqgJ+IKwv7B+CH+rlmsAgwSl5Yg/ZcH1D3/UhvTH8F5kAxC7gmyCIPtFLeqYPPeNXv/pVtgfa3ShqGPAH8ZwBbqOBHzMJOQ9dpOE8A8CrRwwNpQkw9xyGiqJjsnqF4hJCysFYNYvaSvXrQB5veMeKOvMgCTlBc4zSEwTGx7MQxeZJh8ITOkYAb7yL98+hADj44l7XIMJF0boaTVcBP0MJ3NRXe4G4/uHp/uIXv8gRGkWOSIDHTrkRheHIIY4dA9ITx/yrgJ/xYwicA1hkEP8AP6MIcPUfORSN4qH5MbPPPns2dq4jE4wGfvkrAuDkMZIiHn0R/e53GZxEFhxDPPeXsQV0traVRvYsulqeb0C3ASGjJsrkQNNzwCSNH+R5jLq/2o08T1YAMY4cjHLasp7qgZ8ekG0GT9TKSAI48qS9kUVkAzgBnJqY74OXeFBO9as/kGHg/FYYPPdybMirNugDPMKLSC17R3yrgWMhc9CIKLot4Bfxc+wAI13l4HOKHZelZPTVNfgSew2wJ9rHeXYMgJIZfdFVXa+nKuAP50+ARefZKlmLkHEFAWwypH3ahM/6DCgiMqQf2BVRLx5oA0dSvwJQz9UHeEivOAPNpirgp3ei85BTvAfWoZ9Ilk59ZaYFdORJ32ij66JvZNs4esFT/LS1OFnV52yl/sSrwIFGUMOBX4UBcme9kyrgd2y++ebLAg9MGFEgAUgIGK+ccATTEWNBuRkLTAa2FAmoEiqdwjg1i6qAH7MZdHXVBrxST04Ao609AIAgiDAIHPB2ThQsguJpUgLGXse5TucRRKlVmQx7josSGMswyrztssB1lqqAX5pL36hDmfQbb9VxhoGhtemOOutjRI5+/etf5z7kMRuOaVRatTupCvgpuv6LsUwkTRiRECPKWWUk6BIe4gFAcw05AEiMimwAEKRX/rqW7DAs0r4yWVNNNVWWqzLJ/nz961/PAC3F7fl0iXFlOOlCeViOLOqzeoMA0Dll3inCJIscTDJL1hAHj7MdOmyobXzGqh746aN2qit5ZyQ5Q66LbANyXNo4ZFpGS/TPISqn+mVEZDQAd5k4V+qtz+gSucQnbYq+ol/qQX8BMqBpZsTPuHNI2Lcf/vCHWUfsKOq4oTEAyW6oM4B3ni45LyNDHvSR/uHgy/6Uo8pGUBXwC6LwTTAWxLaSfc6L/uPwAj48dUzWVl/LvLgXkUvXcoLJvwxNyDq54/gCQVlEfew+TmKzqQr48UF2CL8NsZAxv11HTsgu3eB00g1BJj0lj9ovSA2SoYNNZFJ/c67JJjuLXFu/I2ojqOHAz4gxJvUGqL1UBfwiQMZKlGsCEUYxigyBY9KBPN+YcIQwUWqT0KGIiBknEWYYkWZRFfBTegLCi1Vfhotg8+h1gqiYoPFqOTEMFsNkeIBhFwm6n3EgQKIdkZvrGEBZArznVQNSBi06VvRJSbtqDKqA34QjQKauZTK0EWNh+K6tQJ1RCMMHfBh1hlv91bGRY1ndRVXAT471Wxn4nafMnCGzxvURfQIqIggRNt6Qazxh7Bk4WwozmPrdOStZ6Ir+jms5wfU6CRQZI4YVAX/vJFP6RdoR2Acx4PSjfvKrrA6ZdI7cAl46Zjc9TgSSJdCXdJSDa/LZ+Kge+CMbQk7ogvrJcnGQyXcAOJ5ycsm4a7QdEDKaZVCSCQM46lkmjqrn4gHeAXa6hJcB7jIjZbmMNnaV2gJ+7QVu+K/P6TnSV1LE5AVf9L+/+OE4kGdrgCj5CN43Y1Z9FfDTX3JbDqLot35lYzm+zvurTXQCv2VmgGZ8x8OzObsmCQrQODfRVrZFxKvftZWc6KvuCBCqgJ/8wCDyo44cUXaLTdR2faMP2Gt6pL/JIRtYJUtwAn88j26VhzplRspZrEZRp4Gfh1xVIcDCuFTNXmwPMVS8pLIg+Y3ZwJ4RoPAxlskgMSBl44oY2Pp6AFTRSvnZzSIKTJE5QmVSTwaJYxRRvzZoT7QBD9XRcRE875Fi8+opHmCUbvJXGxGDIqomaNFmzwgQ9exGRCyIgZdxKZN3EaYq0l58D1DyNzxaisXJw4uycvU00j6GuEz6tMo51g90R5qvfL48sx8Yc/DoWhAeiej0ffS7Z4jYHW/LqXN9eQIf+SAr7qnSU7JLnurJM9xTjqD1ewCBSIxcGGYqO+FtEScnsgVB6hOZMEYvZBifoq5+h6yRHZEUXam3R2yAtlTxRd/QFbYkqDwzXR/hPT6NL2vRGbIVdP120PoobJp2R/u0P/qu3P+K9pblRzaTHGpzMwhIAPqyzNDjkMUgdYq24J06Cw7IB76GnSvbLzKkrUH1bXWfv0HuK8t0s0hbOSvwp0z6CO6wbfpB3ekhR5hTyrGUtQjZgQPj6xvYBPDredks0i6BWoeAX+VFlYCNIGhoFP/z3uqPt6cYg+Rd8fxE+bxix6UyeUkKz9zfOMezFW1RpPKz6uvBY5QSc63j/i9f38iibjxeQiCa1644V25LtEd9XKMN6hV1N4ZHeEyAMblJqouhjGu9J+5xTCd6nt+OxTXxXiXq0ZnimbxXHrj2lXnrXeV2RnFNme+Ola9VP30Y/Ihn9qSiztLx0vJ4oTge/RT/R4m+KPefEvLqN57oT3/979qQffeEDNf3e/k9Sjy3fM5v17uv/p5yPcrHlbhPHdTHteoSMua8+rpGn7fVl+5zjkNL18vXld/h2fF8f+M6v6Pe6hp8ctyz41mub6st+O++aEtcG/c7H22JPqh/RkeLd9Af2QRp/TLvo03B09BVv9VPqeeL6/11j9JM+6Y+6kzGZaDi+epQ/76om2NlHnpGFOe0yb3uiT6O4xNqq/tcF+9sRvEebZVJYsfLcqQu5AfP/dY+mVDDllL/7DX77x7PGV/fxHnPC340s6gTbNGfVVmTNoGfV094dQYvRsqyEYUHBdikvTCDB1Q+xwPk3cdxlfa/47ytuFZRLx69a+J/1/LgXRvHm1HUj8BI+0h3qnvVdRMqUmXqHQbD7zJPurvgGeEl0NqnflXX9bWiX2RBjMX6v5my1RsK/iiGPAAJ/lVd19uKdkpvG7bh9NTbrEm5qCtgI+NsQF+QcW3UX4ZTpMXHJ6euZZvxRiQNwNn9SZFP2sHREKyoXz2NN9VvzEl6QxqtUQUZtzQeGWP8cVxqRQpQKaf0pEikFiOFEtcjaRYps3i+dAsnIVJ6cbzRBXm3mayRvq66rj2lnqqu6c4iPRdj/FXn+2phIAzHSKFVnW+V/ywcc5mjqnO9tRhC4ciLVicFfW5vQaJaAYj0ftU1vbFoKwdVSr/qfLlUUdV1k0KBi+YkyTLU0wQn95XHXBpFxg8BZnkM2fidSQ9mi0p9m/DCEZAKMgPSzFbnjXEhY0jqZ+ZnTO4zLGGyjmstKymPUzaDAKOxIXMWehMZi8Pb8thzi9IXwE+pWtQ+EkU2Y8bypEwcQ+lW2dKeRoDfPJaYN9QXSFsBf2+z4xwa2ehOAX95YkmjqGpWP4Ez0xWYmvRhnaMxFyAuXeG42b1mU/KonbMkyZKLAHizLzkUnmt5iNnDzSQRf/2s/t5ABKZ+Vn+Lqmf1t2j8JBVantXfF6itWf09gaSv62f193bS1vpZ/b2BRPwNXc7XVaoCfpmFmPFp/MQ6T+BubavUPfK/daAaZKyFEbZGWjoRmVEqxW8ZiLXChLiZ1AL+vkUt4O84tYC/Z1EL+HsP9QjgD5KutymCOQBSL9bDx859onnrlUMojclI7UsnBhkCsDxOxG+tqLE2kxRtrhDLLhpFLeDvW9QC/o5TC/h7FrWAv/dQjwB+YGM2qR2QwrjKABjDj8gdgJsDEGOs1gjbyctMftczMLGmHtDLGgBmQwBmFpc3MWkEtYC/b1EL+DtOLeDvWdQC/t5DPQL4pfcnm2yyvJc20DGub0MF29fGOD+QL08UAvw2vAH8nAH7Q9thyZCA51hqJ/0PyJRGG+wW8PctagF/x6kF/D2LWsDfe2iSB36grXIicxPy/LV5kFQ+ELKs0C5J9UbXqgB7AqgrAlQifVtEGhZo1C52bVEL+PsWtYC/49QC/p5FLeDvPdSjxvgbQd213KoF/H2LWsDfcWoBf8+iFvD3HupzwN9d1AL+vkUt4O84tYC/Z1EL+HsPNRz4jb3Xfw2rIzQ+4Pfc2KQnSB2k/as+2OBcWUht+mNDH41uNrUF/OqkHdppjwEfCfEBi45kItTf5Mb4kEhV2xkYH3xodFvHB/yWS9rGtz1K4jnmYbhWGzoCmDbV8K6ObiRiTocJnvgevDckpE/KG0ZNiKr6qgr49Y+P1ehrH5gqfwRmYpI60i+8xwf1U/DByhd60h2ZsbaAX7+SDdtr67MJkQ2+zOXxSddmbCrWSGoL+PGdrER/kBf90UhiD+LjOZ2hKuD3PLumlj/cwzbFh3h6OnUX8OsbuEA+uoMaBvw628x6nyP06UGT52LtfUeI0NcDv+f4JKNnKz4uQOAs1fNZTcd8OKYMRnaYsstf7LgEkFxrMqBPKnpPM6kK+HWqT1IutdRS+bOTis8xuq6sOBMineVzvAyneQtVoOX9O+64Y/6mdSOpLeD3bQWrKnwa1G6Kdlcc35emyIdNmXze1YqN+i+r1ZP+9mEJYMD5M1ejPV+AKxPD5Rvx6on3/tq9St0p94RIX+qrKuWsAn6gZiUKmSOj9pmwC9jEJsBu90rLXX32lL7ihTbQ6379+nXLl8+qgB+PfQrXt/DVTT+5ri3i9K+88spZ3uyB7xsSkzK1BfyWF5ugrN3RdvLaKOLI+SCSd3eWqoDfkmh7o5RtvXlY7dGnnkDdAfxkAVaxh+ar2YY+iLw0wxloGPD74pJvYhMEa+vnn3/+bKg7SlXAb3tLy+9sxoNJiy++eK603foIs48OYBzHA/kG/GyzzZammGKKLwCeoXMNI2LjH/c1k6qAH7MZMiAQ3x2wAZGIRdTPy/dVKsYLjxHAi69SudZ1wEobtM2KB6CJX5Qa/3nbwJFhjy/G6ZNGpKGrgN9veyP4Frg2cLLIAplAeKC/4oMQ2jHddNOlLbbYIvcdOaFgHBjbmbq2HtQB/oILLpg/4OQezhOA0rYyj70ff2IZZ5lcv9BCC2U5sgOkqFLdyQQZE5Hb/Em98U+/4K+tock6p82OkOpST1XAD4ysIPF82QXG3Ze7OKwyT/o1NpgCYPiAd8j7yIfncmIjW0AuGF/1je9TiJB9fMP9kREjE65xjCEpR/Bkxe6XJsnig/eQD8bbN8N9d58j6rk+5oEHIY8cadeqQ+yRoc3qj+/6r70OfxXw00t9qz2iXw69r6Mh0b++wGd9o34CgmmnnTbrDGByn/pol8m9jnUlym00tQX8YZ/Ilv4gm9HnZMd20PRGP+p358kPx56DzYDr78iQ6H995yuneKE/OUg+JoXIPdkgX+3NClYBP0fW6qly8MHR1S/kLj5nS+ZEtOwi2Vc3to6s+Px0WW7wCB/0n/72vsjWhpzXfya3WdQW8MssqZu9ZfRFkHqRT/3CPkd/sH3qXZ8N11ZBzEEHHZSfx47aaybISjV61WhqGPATvgADTJpvvvkyCHWUqoDfswkMsic6w3DiiSdm8AvjQ6AJNkZjMoEEfLGzn9n/HAbAYf2/DmsmVQE/0Nxoo41ydEXY49OThIhCATbnV1111ezxx+ePtWvbbbdNc801VwYPSiJKo+i8bYZ4/fXXz9G2bAbAAZz4tOiii2ZAXmyxxTJIdpWqgJ9h3WqrrXI0TS6AJyVlfLyT0wZMOAOHHXZYVmjAr56MoPoxbhwB7VdEygxWEANlC2bXUn4bOdnAyTcaPN/78EVULeLwCWMyUvaWydUiiyySDRgZ4RwAD46ieyjsNNNMkw0wkLf80/u8g9xoo3pXRU1VwC+roZ+AFGeMPOhjsqdfOMeWlzII3iVDw+vHQ2DLeRX94htgwCNGVZ30N5A0nGC1i/vwk+xwXDyLnOGppayxhwUiV6JLX1nEBzLor77VT9rqGs8ghwyT9+pXjiu5CpnCcwDuPIDm6GywwQbtSrlXAb//9ZF3k29tZgvYGjogm6Q++hd4ahu9UA98OOmkk3L79aG2kBH1qncCJxa1BfzapM7kiI3jwCKblUUGTXaQrLJtHMhNNtkkt0/BG/aN7OC9L8rpK8d/85vf5PeRY44Ae0HebHEuu4DX7UnNVwE/WZ5nnnmyXfJsOkYu6I9ClhAAE82yHZxOtop9Vjh3ZIo86ifPCX0gpzK8HGI7rtInmVty1tGMX2eoCvjZNTIl8MRf9WKT2CB9RDfoIQzEa1lN+kke2fIIiBBHjoNHvvGQXYCb9Jrs6x/6pP3jy6B2lBoG/EGiMcCqE9szPldPVcAfxEsELlJ6mMLg844Rg2HnvvBeKQ7HgHHn7RIenikmEibXl0Gh0VQF/ISIAZ9jjjmyEjLehCe+UqZteOt/gk1gKD1DgJcMAzBhOBhkUQCDSyicoxSiWoIlAsMfwgSYGQHGoKtjt1XAj7+EUnvVQTvUjzJ4rzrrF4Yc7zkDFMR5bdNv+tP9+OCTpQCWkQkSPVAeBkTbgIN2kkE8MuThPL457r3ksLwxE4MiCwQQ1Y/Saof3MaJAw3O1BciRF0pM8dWTw0Khq5aCVgE/z100CqQYMvKo/qIBoE/B8ZMxcI4R5VxwegzleL8Mj+v9dr1tqDl3HFl1UhhexpM84C+5AojqSk8peNloiKy8AyBwshhSDqRohTMhc6Y9M8wwQ9Yb4MPRUicgo/8BhXsAlv5wjLOibr5FXk5XtkX1wI93+OHd+K4t0W7ADtg4+nRBZsK72Qr11yfqQ9Y4+/ij3Qyn+9ob1Tab2gJ+oDjrrLNm/SGf5NmcBXJBdjiHbBedBoh0Rpv1F/DkXNJ1cg1IyBIHwdLl2WefPYMwmcBvTgQHVH8Bf1kscjQhqgJ+Mk9O2DH81y/sG9knL8AOqQtZ0z/qC7Q5vcCRLPXv3z877RxO8s5p13/shPZ7r35kM2BDODPNpraAH185Yf5ywvBeUEMWySdesT/aLcvIUcB7Mq0f6+2wjAl5do92Cwzp8UwzzZQDAFligWKjqGHAz/hTTqkK5zpLVcDv2Srp2cABMV48RwxCnAEAEIZXtEhJeFwEi3FUP8QoOFflXDSK2kr1A3xpsHoiIIyUCJRSMACuowwRrTGGUvfSmhScMlAMTg5BE7UCP4ZDh/ob0SmlBPxdjXyqgF9KlqIzVIhiMFjao/7RZ3gR2ZaIIEWa+k39tQmAaePMM8+cU9xBAfzagx+e772IQZMZIR+AkaIyiJ5V7mOGhsEUEVG8UD5GlsFynzoj8qWeEWlxDBijOF9PVcAv2gG+MS8hUpkMGwcl+hUIM4YcD+3gNOsrRgThm3qTdcYOfzgL5IGjx/BwGPxmPPSF/gfanoEvDGYQZ8KxqhQiZ0jkzDmbcsop8yZZ2g3QPR9/OXDIe7xXP6mPeukLhq4zwG9eASfMR7mCvIORJ3PqHDzEJ21jfBlSgAoQ8U0/ky8ODJlhbDsThDSD2gJ+mR3ZGUATcslpFU2SP7JJvwEI2dYfCNiQA3YDL9kXwzHOk1nzZwC765xjWwGwPgK2eOT/yIyOj6qAH/CRCYAdpD/IAjnmWCAZWcfJE12TuidjslmI7ugzdkXAw3ZJf7MN+tB76R650lbv7ExGuaNUBfz0lpwL4tgOTrE6a6+2ITZZ3R2nn/QKD/SzPoFpiD2NrC+CTeQdtmon/dLn+Bv3NIIaBvwq//3vfz9XmjAw9vXjGe2hKuAnIN/85jczWBNuEQil4E0CC6DpXIxfIYKsLqJnnUd4CD4B8xtDqyK3RlFbqX6eG4dFGxkodWbQCQiQEJ0DNMCm3QCbsHgWT5pC8dQZAx0nmqSQrtEnBJGT4xrnwrhTukakPKuAn6MCmCixvo/+oDAiDaAmWmagKK/oXmoPAEbEzzCJePDF/T/5yU++cBgQeQAuFIvny5hz6iiDiAD/GBXKBogoGhDghAQBMI5Hfb8DOqlHSodnKLIV6iBykYLj1TOi+F1PVcAPiLS3HnToiuiFrCMOnr4X1XEW/M84cJo8jxOLX7IF+hGPgYDsRDiBdEGfOw88tR9o4qWIT7YjiBFh+OlB9Bc5xE/GDN/IoH7Tf2QIyGof8FVPBHy9j4wCKGPOwEu2JlLV46OqVD9HSxRIjp0T9ehz+szYk2GypP/Vm7zQBTrO6VEX8hApVW3UZ41Mk3aF2gJ+tgkAl427NuGnIADwaTv9FzWTTUReRNvsBn3TT+RrxhlnzP3vPkNk7sUn/CJH5Im8BPC3Z8y8Cvi9U2qfMxkEwNWLTrJ1+sv7vN9xusaBk6Gij4gcknfBA7kD/HhEH/U1WfAbpmir+7pjImcV8OODSBzv1EGQwvbgOxtILvUbvaMT9JNTQG84DJ4XpC1klB7hk3aT4bAjnGC2pdHUJeAvj+Mx4Aw7o8XLY4ja40XWUwB/ebIIBQbUDBJjionSi65htIEeo142sOqIwVJgiBdMYTCVMYvotFlUBfw6k7IxytqhqI8OB/4Mq6hHqo4iEHJGgoGlRFKolJlRZYwBKCXGB/8TNs+kMI5RFtcgAlqOpDpLVcCPRLQ8dEZaf6gXI0dGACajTDnwg3HTN/qAB8+R8xdgidK1VR/JCAThHUAnX3glGmdsREd+Ay2AJvODp2Qw5n8EcQJFiGXDhciuZ7ves5AsETDEUxGWd5Ip7y87JEEB/OUUHgOhX+rfh1fqETKuvwOsGWiAKEuAh8h1rndfOFDaxzFEhrvUC9/JB75yjvDBMz2nPIarPvSlXg4ZKVkxBkwfGUKia3SPvms/mYpMi3eIusij9+MT2dAHoXfjoyrgZwjxF9+1kxzEcI1hRPWRFiczjJe244n6qou660fXsRdkrz5ImZiknlXATz8dLzuOSNs5xfqHA2oimSxZOF/kDhizG2QGmNI5NtQ9HEiZH+8jB/hFh9kX5+mlPqerE6IAfpFokHcCurIzDVDoFBvIdpFpuq99gMYz9B05ItdIu9RP/wsIyCR7zQFUV7KinzkM2uq+sm1tFmlrPfALJgRtbBXe2jIefzlPbJci4/LrX/868xv2kEV6xEbWO1n0R986r7/KTlSziAx0CvgZDumaRpMJEgQgZrS2hzSivdRdKT8RKqPWmaxHEB4w8JST8otiymBYTxSirJTNIIZL37c1TOL99cYLtbePJtQ/ZWBtixrZx0BSm8tU1T4ZAgatK9ReHuFxlaFmTMuEVx3Rjbaovc+of/+ESGAQjlYVtSXL9Y5UFemj9lw3MchQiWCmI9RR3qIJ3YM/7dGnIE5Ve52EMrFLHSU612xb1h7SVk5GeXJsUL1eRJaQsylTxXkpO9wT6o/O9HFnSZ9wHjsE/ABJyoln7mZepr8MX7nEufYW0bB0q3QfZvvfcc/xu+rZjovWqt4V95f/51nXH2908XxesKhD1Fh+XxWfnHe83Ib4Hy+MAces13hWXFt+Nj4o8X/V8+L/zhYRrGwOj7f+ef73fm2qOl6ua9Vvf+O6qvr637Prz5X/91cf19chzpXfWz5eLuXj9e1xf9UzOGaiG1F/HKt/XrnUP6P+XfX3lq93nRL/x3nHyvd4Vv2xKK53vlzineN7Rrke5WvL15XvH1+RGRDp1F/v//r3Rqk/7m/V7/J19byemIX+sJ0iP3WM4+W615fo22hH+drx/Y574nj8Lp8v12F8xb1S22RcpBjH47nla+N9fjtXX484F+fr71Hco5TvKZ+vf2ezirbKWJh/U36/3+V2+QsPDcsY3jB0B8vYItc7H9fHM8plQucbWdRXUCpbIZNST20Cv9Sjm3iAvHYz5xtRpEVUioBJK0nfls/JBkiL+O2YNJN0kWPGQeNaRb0MG0T9/G3r2kYXaSsevWEInRn17WiJNrgfL8rtmRgFv6V/pa2khcv901cLnvjLEZLiM5wQx8gBmS0XfVi+f2IVdTQMNDH6MPjD+Ejll4/RTXwqy7nfZb2vL9EW/K46Xy7a69p4X1eKZ3REJ10nY2c4BJCU7VC0u1za056OlPbyqKq4TxAjmmUDJqYd6q6ijdpqmIIdn1CbyzZaf+J31XUTu6ifoUtDYIaN6mm8qX7eTHmMv1EEnI3fxfhnkDEkio+hxoGCjA0TyqrJO8ZKyukYY6Q6pzsm+hgDNzYVOwf2FpIiivHeFo0j45Kc1nLq1FggAx4gY+yZPFQNFXQ3SaMaayzrUncTJynmMQTRTZNFy+lhv9W1LXsjHWvoqT1DPOwI21I/fNMZ0o/0oCPDKd5rLkL9GL85CGxfyIq/HRnubA9pd3mya0fJcmrj9JNCCr67SFs59eaU9SYis7KTXZ7c1ygi/PWT+0wUsjTF7EczRk0kMnai0mbwmglpNnJMmjD3gGMiPc7wIhNDzB53rQlocbxZBPilfnob8BsXFLG0gP/LZDJT/eQ+wz3W60v7SQGSVbOaOwIUzSITIU0kEklPLOII1U/ukyWzjLA8EVVm0Vrx8mzoMpnMaGZ6ecliWyRyM6mtPrDoDHE0zFAXFbaXAL9x8lgSGSTLaW09+8ZGWf0j69lIMllS6rqzpE/wr68BP7nrbXacDerU5L5mzZSVKgH85cljFIBCUHBpCmMo5gBQEGOrjseSCYrF4FrCY1mR5yHpaZ6bultqxsA0k6pm9fcGIjBVs/r7OlUt5yNzlvOJ3DgEIleOk7/AgnMqK0RWgAiAI6PknfwbjzOr2V+RmmhV1MUQUVjRujFE6Vcz7L0bkDPQdACohh5xqE3KFGG7luMreiOfnuV677HsL3Sm2VQ1q9+EqP/6r//KM5zpsgKo/+d//ievfGCIDTNpMxtADmX9LAEz2x//tM99CJ/ZKo4/HvrLAcPzIHxTF/wh2zKISKpWX3hmecIxXkp7ux5Yc/raS7ITVbP6zQJXyIG6m+hFVhTtVS+ZAu0XCBlKZF/0mXpql/9jPX2sljGPwv2eZ5OvcJ7YUfKnXe11gvDCnAB16iukrfWz+nsDkYdJHvi9J5SO0tg9jKGy1p0AI8ppHagGUXaKYN2vtBmi9JSCcbAmmzI0k1rA37eoCvgtrbPW3l88U2LTIjvSGTu0rtdGKoy3fRqsk6dbHFozgy0VIq9kCSDNMsss+RqgY56NTAIDbo20oQYTkazR53TIeJmMBeRly2TNgIv1xe6X+eIkiEDNT5CZ+OlPf5r/7w6qAn6TWe38aO22pWz0CKhbtx3b8YqGzQ2wZ4MVLzJ9ztsbQeTMsecQ6RMg71ptxQtG3Lr3svziPf7gp3M2V3KeE2JPAStrPFN/4aUgxHJD0f5UU02Vlxm2l9oCfkvD9BcZ0Rf6gIPIwVA32RkzxsmGYjc3gY57tN0kSZOAyZrhEksr1dFEQvJDNuwVwLFTX/LCqZQp5VjFxkjjoxbw9x7qEcAfxLAyagGoHABj9kjkJJUfaVTL6BiM8lpuQwCMACWmYMCfsoseGg1kLeDvW1QF/MDbrl3AGOjYdEd0yBm1uRRDbHY3R5UDa+Mlv0V91ui7n4MLqIGPCTnWBgMfeuLZwI/sex5gsIELgFcPBktWDCjIiIXzy+nwbMZflgGZO0OfHG/PWHkjqAr4ATl+yZSIRjnwnKNYky6j4S9A5OgDexG43/iAOA8A3PJXoEdeDQXYYc1eB5yfcsQvguZgySB4lw1ZzD+Ia0XZnu0d7AW+AWVzETy/yni2ReMDfk6GtvstWlcvYM4Jsmsix0y2U/BioxigRKYEQMAeTzhFgiRGnQOhXb4/Qc60Db/dT6Y8E/DbOIuDgE/6v620dgv4ew/1COAHNlJWlJwhoDwE3f+EERFsnniMsYqOKAwjywjydGMtJsMhapIlMF/AuKvhgkZSC/j7FlUBP7kCxFKpDAjeSeP6X4qWDolIRWacUAYcMJs7A7w5BiI1jiow8w7XeBaw4yiISkXBZBpw2QGMLiD18X5pcwYeoFN4O6CJ9ugLR4POcZJFi3Sru6gK+EXm+Ab0Rd7S06JfS0iBk0wF4KW7nAG84bQ4BlCRdsiE0H8OBB66HhBygkTqZeD3fO0HnN7BQcAXGxzhK+KIAV5RP54iUbJ3GFpoL7UF/GRA2wUn+oljwSYBek6LNL+Mh7aJ5skH8r97XY+X+l8GQ/vJBrvHORHwaDebxJnh5Gk3vmgTncZ3zyYPVdQC/t5DPQL4CZvtgHnclNO7jWHZ2Yli+58Clo0I4JcGMzsWUQgpMwbFc6RCwxAqZYPdCGoBf9+iKuCXdhahM7pklPPKcQX6InXyIQoD/gw7Z5RukW1b4DLu1vWar8K4AxjXcA5E78CcTNMPACnlHyltRB/oh7r5Kzpk2A0XAEFRsXXlHIE555wzZxcABdBrtD5UURXwAyVODv1XJ9kJ7QXy6o8nQNokM2AvA8gJsm2sCJ/tAGoiZs/mGDgmY4KHgBxwl4EfLwyz4COwn3rqqfMqDdmHcKL0mX6SdTGE6DmCBh99as+kwqC2gF+/qV95cignUQaAw0Fu9DfHhVxxCpHfHAO2LORJVskXCzkxjpnrxN7JBtgwSTsND+ARvjguyvcMJeZH1FML+HsPTfLATxGkIxksQO8vISaoKm/CD6NAoMsC635GLABK2ox3LH0nMmI4m0kt4O9bVAX8hpmAlbFYcisCM6xEFl0rEmecyQhwA1QiPs8wic/1nAX6IOIk8wAgZNdyVfLvOfTRM9wXk81EfgDLMlagLrIDVhwJwGlmufFedaJToV/eNbGAX/YiJirikf+l2tXV/Ag84NioLydeCj74KU0NuNkC9yDRvtS26w0TKMCrrP/4JiWu7fhEbw2nmP8Q65zdx0lCjknHsyecrbITMSFqC/i1U6nnu+yPOkX2wbsMQ8Q8DHXhNIr4LXfWdyZ9hhOjfuydtuARJ0kdXKcN5MHz2kMt4O89NMkDf0+lFvD3LaoC/kmFgLu0PiMPBE0aBHYTm6qAv7dTW8DfE6gF/L2HWsDfJGoBf9+iSRn4kWhZShvQTgqgj1rA37OoBfy9hxoO/ACvK5tjjA/4TXaR1iuT1Kgx0fLYWJBzZSE1FGCCX/1nWZtBbQG/jYcsP7ITlCJ1yRBX1b8tAr6eYzwOrxmTetJW/ePaRtKEgF8fMej1/dQZwpeO9BU+kBuzrYO3loRVgbFr8UeKNCZUdYWqgN9z9VO5b/Gn/OGOzpA6m1hW1e89iaqAX3/TZ31HT8ryq7+aEWx0J7UF/FL6dLksK651rC3ZxBv8aGtMvtFUBfzqRhbLcq8tzR5K7S7qLuAn94bkYoiq2dQw4NfZJpqYiGIiEfDuTCOqgN97YiKfYixPxY1NmahjyZJZv+XPGRrDcpwBQYDAzlVmu1r21JFNNzpDVcBPkU30MmEp2mJCkpm1HTHiOkvaluE07lflaHm/iUEmKTWS2gJ+dTApyzIjPDaB0phyV0BOhGoylQmaxqonRPpan1vWqQ4mvKlT1ba0HAJpb5NAyRMl6ApVAT8+6W/HGGfj/SaZxmdmO0vGm81rAZA9maqA38x1q3X0naI/Y5c7Y/exZK+nUlvAb96CdsfmQUi7rTBoKztgLoTJeV2Vp/ZSFfCrm0mQ5X0A2GrzEnoDdQfwy8aZaEveTfI0vySI7SjblEZRw4DfOCIlNWkIcNu4JEC3I1QF/IQrlub5TUFMtDFWaUKOd2NazMAFuDa4MOPWZBZkoosZr5hsQhUD0wyGBlUBP2ZbTw2UtFMxIQeIai9+AWptjPbzqCk4I2CylihY2y3r0jYblfCuHXefZWEiTd4jAMQTxyx56khWoS2qAn7KAUStOVZXTpZJZurIyAFe7TTByDkRK6cNUPqrvtriL7IOOSZh6S/KoC3aj0f4iMih/RqiHzkJdlKzIY77Xe88Y+ta/CGfeOu4VR8EHxhb3qZtZmi7RtagI5FUFfDHMjpGkZzij4mqHGJt1C9m7QeAe5/2ajd++B+vrPH3bBO59CFHyDIvekIuyBrSTtdykvCibKAnRaoCfrP6OY3OaacdNq1wsF+HY/pLP5EDek++gn/4oP14oh/xDnE+8Zr8+e24/ier+B3XdQe1Bfxm1n/lK1/JS+qQOpEdx0xiJAt02GRFQQs5IxN0DC/Ihcmk7EHVJ2QbQVXADxRj5UWQ+SRWH3BIwikRGLBJ7Abe0xftEDDqRxMb/UZ4xAGKttIXNk479Zc2NiKj2B5qC/jJkbrpA3YmSHvZD20C4GGryG5bfQP0BaNsAztnMnCQCer40GhqGPAz7v7XWIBsiVBVJDohqgJ+RjIECLMJmtmq5Q16rO+1REo9GD3G33mAo5GWrwB/HjJjQviaSVXAz2BZeiPSt2QKyGsPwCMsliZxTrTDmlt7FXBS8NJqBEt0zMA2o5fzwMDxtikJ8JL9sNxJNgBwATYOmGOWO5n921WqAn5GGeDqN0RxyQLekwEGwyYh6gDo9BUHSN0tvZIpko2xVBOJfgi/Y5ROVsS9DCDeRTs4BOQwABpP7fqmfupE+aTQGAnK5X0RSTI0HFV9IHrGV7O0rR0325kixuZQ7aEq4NcP+lL98YfTg8iGupMF/WbJGTkRJTHk5BQQMHLarJ4yWjbpCSOEn+qqb2XZgIIZ/4yu+zkZzd6WuqtUBfycJf0UxGmyjE5WhrPOQcM/zj+9cD3+sDsyTJYAkh28oX/kFJ/xG68tmWPsZKasbpCF6eowT0eoLeDXj2TXUkFAR/a0z2ZCbENkMMPBttrDMxyj6+TX0k6yy3Hi6DSaqoBfFmbeeefN9pj8Ayp9I+Cw2VE4Mo6TYfpKl9kqS1MBnf5m0znH9Icc01FtYcNkNemC/QjoBdun/5rl4JSpCvhhTGwSRf7Ui63hmJBVDhu9950O/cge6SftdBwfy6Qv4ZOVJ2QAr7yD86+tlm7CuXJWpavUMOAPIow6kzGq95LaQ1XAH8RDxxhGgKFjMIELYkC8FzAhTGNEMR6A+sgHY06oKAYHoJnjKVXAT4gIhV2zAqjVSaeqvyiOoRIFawtlBhqcA+0iVNJoDAfg5AnGJiwUhSASUsAIZICqZ7jXewBRV6kK+AmPXceAHxJxqh+QtpSIIAM+njGQZiD69euXjYY+ZJil9Tk7HDwGTz9pDx46x/ERBTGQhjD0L2Oh7UEyQja1YXg8iyEU+YlGLHtjQPHP2nrPxBPADygt58If9eGYqHc4m+2hKuAHvPafYBgYR8YBkW2GEqjTI7LJEVEnhi52keOcMuCu4TAxlJxqkTBDIauiP7QT6HEE8VVGiIwDEUAzqVIV8HPCOHwRKdFRoA3wgIhIEtBxaEVIHDvngb1ryBI9A1BskD4F8IYI8JfzpL/JDrnEv0ZkwtpLbQE/u6D+AIIssIGyH/7HI7IT4KpdjpMJdoSM4Id2Meb0nu40ul1VwK8+008/fQYo/RD2DegDcm1ChjMFNfRPQAa0HaOLomd2i8zSVbpE5skxm6fQXbZf1kswoR/Je7OpCvjJHLtER+kwm8O+wCV2j0zhFQeHMy+ggzfsnb7RZ9E3bJqAgz6713k8FryyYTPNNFPOXnN44F+jqGHAz0iVjTAQ48l0lKqAH1gAOUBOWBDvxztina138abC8HIMGErRsI7ifcU6XM8ArOX0VKOpCvgxm/CLMupJnXQuQWPYAqgpgxQZAn4yAFJGhISAUAppbcbC9cDWvZSHcWBkUGyAUgamzlAV8FNiwk3REbkATuqiRCTqXtEV4w3AGAdDOLxm3jJBD4+ZjFEQsgAIef+Istu4hfGzzr08iYgsMOhSuihAD8B4P+BgiKSOKWoAP74CBWlidfJ+9Yq10u2hKuAHRiIThkq/kUFpabrCUKo/wBa9c9bIirYy+CJWEW5sOMOohJPEMSQX8TVK9RdNyJzgq3kF3ksfIhsyKVJbEX/0NcIrRp5+iCLJsGOcmkjxO4Yv5E//IfKHh2QK8OtvNgKfOGTkzPu7m9oCfrIumyFTyc7JTJAn7eLMyYbReX3LMSa7gJ89YdeAj3tcR9fxoau6Xk9VwM+h0heCqyBtIe/kH+Ah4ClyJe8cBFlOMk9OkbayV+wmbKEf2kqG2RDv9dfkWM6gdzYigzkhqgJ+esc51Q76aR6GAEt7HUOuZ6Pos621ybS+oat0OfRScEE+w2bBDA6DLDdb6T0xf62RQ1INA34VB8QYwRPigXYm3VQF/Dr4O9/5zhe78zmPYZjJKPKqeEoitSCKL9KKoQBRIiGK1Km0SiMZWU9VwA/4CDQ+4RFjRhhE9IBH/SLFw2lhICiz4ll2F6NQFN61Og4AUEgGU/sZD+lOUTelcg2ijHjQDOBHlMNQBGDVR67RJ+QBzxkzgqxf1U99gTheMGQIMBvTxBvkuOcy2pRLdgd/GPNvfOMbOeItE/CVWQglCgKg9ivHTwZRhOJdsim86IhEGCXOE0MKjCloe6kK+CktIHZMvb0PGAEenjw+ad/Pf/7zbEAZD32kzzgwInmRgPbqYyle/S3tOeOMM2Ze0wVZAs+KTAe+4jf57840dkepCvgZTlGg6JY8M57azHniKDGCdN81fpMVTjwnDe/ICf7hA9DBC7zEJ7zWJ+QBf+hdd1NbwM8RIZuyYrFLKftKz+mQ+gI+9zknatZ2TjA+ChrwQ2QMYDkLjaYq4OeoAKryxGrZR8N1QI8MinTZbn1Jz2QjOAr0xb0oHAjAzsFlowUzbEDIt3Zz+uiS+wRAzaYq4McHWTp6rA50ke1gd8ii6wG8zIc2cNIMDegbtgbPgmQ79D1nn9zijcAjstHeVQXOXaUuAb9KB6mojuRt8qo1sjPRBtAjBOX5ARwIjATWlBsTCQAQAYTOMRQEIoggYnBMAnGtlIr6AdmyoDaD8IixEckGSe8AbXWgpIrfOkBqx1+GgbcH6EXSnB5KpM6MHSNgPFMUzUmiEDIXlIuDwNgZD8Q/fIyJJxSWEHWV9LO+r+eftnkvQCe4ZEDdROSyL+rC0XIdp0TbKY4oLIDedY5Fit39hmoonTRmOJL6nCEpywgSAXKkZEzKxEkRLZIb72OQKCSHxD2UVypVxsp7OJTAoex8ToiASP3KA30nUxXOgP5k+PCJYwS49as2c1S1X//hnfMyPeqJV9qs3uSCTPtNH8iPvmegOEKuZYTIOvkv68SkRpxyfVAm7dUmvNEW7YixXPzURv3LYWQkRVkcNbLFNgg48JhjF3LEKfA8z7VNrWEw8hgZg+4ksqD/69PU9JkN0F9sWWSb1NMQlmyW9rEFbIB2kFnDFaJnNkHmQxvJSr0ONILwnl0vB0zqJvAqy5m2yKSRU/1AptWbngEaNlAAwdbH7H+ZWfruOZxoTp57tZXMc27IuUwvXHFfGYybRdrqXWU7jt+OkSnyKdjikGkv551uAnLDn3RaPUNX6XrIc5C+i/OyHN0hl+wFvekQ8DP6YfwxRieGMGCKgoCEc+0tOlS6FRBQWiDouEo657cIhrfiNyXyXp4xQInrFe92j3v977zrXeuvEtc2uqgHoy2i1/FRd3VxTt20o1yivq7zG0kpESCFMov+gJ+6u1bbPa/M+/LM2DgXdVKCH50p6sXZI9zq5nlxLurgL2/eMaSe5b5QkOdEmj6e4y8q19lv9yqeSzYYd8YAH8rtiff4G8eU4Jf74x3lvvCOqL/rXRfyUn5OVYn38/YZPH0Zxzxfif89Uz2iTRw870flv45HPd2jX8PJjuchz4zjUX88jWPeEboyKZXgB6DjHJePaZ+2BB+DnIs2ynhw/ACf65DhPHrCudNm1+q/4GuZh457j3Oe213F+9STEy7qL8tXtDvaHOccV5B7Q7+dd61zcY/nA0bnUDy7EYUscVY4svgYvFNn56L/4ljwX/1cH/rkeNQ57nWP55XbQo7bamv8bnb/eT65EUwIoOJ93q09sISsRR2BvwyNTKGhRZkXmV/k3ugb95bfg/CC3cEPFOeiveXru1q8S905UlVZrzaBXwV5KKInno/IQvGbh6qUj7e3MOY8HhE8rwlwOu4vA8HLVfyO5/stagAI5WdJt3heXBf/8yr99X/5+kYWdeEFisB5hN4X56It5eJ69fRXvaKurpealMJl6PyO53tO+R6/y7x3LJ4b7w1+drZ4JiVgYPV9/fP8r3/0h3a53vFyPeM69XSN385VXRf197/nSl9SKClBjmf5HYrf5XvimP/x0zOc9854r/N+xz2e6TrH4rnjK+5R6IPIJo75G++I90U94njUJ65xX7menuGeMq/K98e1/rrXcfJdfm7UL55VLo5NjBJ1ighVXaKO2qCtSrQ17otzDKton42ItpINdkO2pl7HPb9KN+J8dxXvVk9yYj6COsY5bYu6+R3tVs84Hm1wzvH4G+0IeXAsntuo4pnqLRJnA9TJcX/LdVCi/o45p87+xvE4F/fGc+K4Ut/W8vmoT9ShWcXztZXdhUvl96lL6J7f5JLNN5zBXrNRgjX1VOcJ9U0z+66+aIeMIv3jRNfTeCN+KUrpJE5AuYhWlPrj7Sk8IiktY3vSH+XjUkD+KrxBx3ljit+8YV5M3FM+p/AeIxpS/B/nGl3UkYcoZSdF5f/y+eBRPa/qf0db43480MbyffX31P8fvxtVpM05M4YZyseD32TD/+rqb/C9fK1SrmtbdW7rt/d4bvlYFMfwK+TB7+hr59p6ZvlY1fEJFalZym/YJY55r3o4ZtggIrbo13K94p54f9Uxv7UpZCCe43e8N37H9eX3lJ8V5ydmkSUBhPXHx1dHx7Q/dLt8Xeh11b31x+rPd1chB9Lz0qxV55W26lluQ/3f8vnysUYVsiba51QZxiyfG9/7JlSn8R2Pc1XXtHVfo4u2ynACyPpzUccoZI++0T+yGHav/rr659RfU3W+0cWwBHvVoYgfM4CahjWaAD7PR+XKZJyEZ8ULNL6PpE+M+/FgjDWViZE1XshIIHU1xkTpRKTGl5pJFLxc195C0lz6vn58HX+N/4nGwnFD0kr18wG6g9RPXSlS/STAZpC0NcNYT+SSdy1DQh7JLFKvSFN3hKS3DYcF+d/cEI6WMXOOpgmLsczVe5xDjgFbxcqAiU14Qx/7GhnjJws9jSyfZEMjHd0XSFvhTm+z4+yyOXldntXfKKqa1c9AWNIjjWcdt3EUwG22t1mU0oVmgoaBF22bAWsmtzF2pJGWvFhKZpJMsxWvalZ/byBjRICsPKufk2Uillm3ZiYbCjD5CtBwBsxsj7Ew41v6Nv4P8tzysQDIoLKxcb9xr7aIkkoFxwZJZkCTWVFimTyz3oHpLFXN6pcFIJfGnaX9rEM2WclEIbN7jfMGRaRaJo4L0I6244lVLLFsUtRowqc0JEUG6GSO42WyFK/ecjCOATK5kzMqagM++mJiUtWs/t5O+rRqVn9PoKpZ/b2dtFWqvrfZcba207P6uwv4zbY2tIBEjya5GU+xNCqMp2VYlvAwgMZlnLO0J2a1G58yTu5672g29SXg99u4FjCT5kImugFDfTLttNPmIRxOmH7iqJm5zqGzfMcxgKbfgwhlABZZ8E5AgaccN/eLQMpAGySjM/nkk+cxLJMhgaPxX6sNeO8I+AJjDossBZnuClUBv3X13i0bgLTfhBq8sYTPjHRtUyerCYwlyl5JE4qE8c41sV4f8ANyxwA4ngfPnHPMc8g6J1g2zLbVjlk14FrOT0Qx5czBxKAW8PcsagF/76EeAfxBQJ3Rt3ZVxCLaF9kgwwPAPsaWGToTLPxFok4OA8CwvhyAMICeqXOrAKQr1JeAH+8YMv1h/wEgrn9cC3wBFPDSR0CJ8bC22qxX9wEn/RKbMSHXWrcuCrb8x54EltjZA0DUbFmMa+ozA4hDEFsIi/it2+coAmLvJeycAOtrLcETlXNGylmFjlIV8FuZol3WHFvDb0khnVEn9WNQZK5MmCIrljzZ7EOmwiQhToNnRiYAaHAObONqrXfsf4AoMX7hCcdXPxhesAZaf+GzY1abGJbxu34pXXdTC/h7FrWAv/dQjwF+YM44M1iieEaVEY+oxWxIwA/Ikee4NsYy3WMyIqNnnbNzDLGNL0RW5XWajaC+BPzS1NaeAhVLfoCy3fH0o3Q0pwtJRYs+OQOcLxt9GArwW5RbJv1k6EBkalMX78RT+wQARZEvA1rlsFmnbfa/oQbgAhDJBZmykZF13oaOPEe0z5G0sVKVE9Feqgd+f8kovshYaYchKrwgk5wO2wmLwDkkHFqrQOzipV3qaVJRmTgmji+00EJ5ngWHKjJh7rWuOMh1hlxkOvAdHzjC0v/mFnBE9M3EpBbw9yxqAX/voR4B/IwV42z3KhE8Aw0oYvcq9WDgjP8HyQQw+NLJjD6wAUruZeyBishQKrg8CbBR1JeAH4/1RWxgY8wffwERHgBhjpud6GQDTEgDfCJW0bxMQf1kTmTG9yyzzPLFzmBS5aJZ/WZYQTRrMwxOQtkBEEVz7KS7pdkN8RB013Ly9D9ZsgTPhDjLwgBoIyN+4+ccVTKJH54tFc8hYkA5Af5GZsO9InDzUPBR+/C5TGQX3wxPIM+zA6CUvrkVzhlqkV3h1JgLYJtXwwvaiY/0gmPkPd45MakF/D2LWsDfe6hHAD/wMCYqMhTJAA/XiRZF+aI1qeDyTGXnbYUo6mOEGUYZApGiyMfs1GZSXwJ+x6SVTegD7rbbBPYAWFrbHvXWj4r2OQicMGCvf/Sh/qsCfg5f7HONRM76WSTrXnMDgJ5ouTxxz7Pio0UMlayOKFf0rf85ewBX1MthJENdTXtXpfo5OJ5tKAEoa7MxfLKtzeqCL5wUTkJMigSGwN21ZQL8rjM3AZFrk1U9X5tkEzxXe/E6NoDCC3rA6eFs6SNZg/rd47qbWsDfs6gF/L2HJnngZ9ykTEWV1lKK4IwFW1eKRIGMf0yACgJGUvuxZIrCGRYw9ly/ZWIzqC8BfxB+AxMReQA5sNJ3lveJfDkClpvpU/1g/NoQgf6potgrApEF1zKcnACZHDJIZmKIJwjIA16ZArIBkCmx3+EkkB3P8rcr0T6qAn5ETp0zw9/HlIJiCEC2Am8AgfOO4y35qZ/lH7pQ5j35lgWLNeyR5YglfK6lL9qsLs5zkL2nfnilu6kF/D2LWsDfe6jHjPH3NOqLwN+XqS3gb1Hb1AL+nkUt4O891AL+JlEL+PsWtYC/49QC/p5FLeDvPdQU4JdSl76UmuwotQX8xntNZjJRqbwTnIq7vpxGBUquM47rviApULOrvaPZVAX8+OFrc9rRr1+/PL5rdrbZ3R1JNWuf9Lm0r/ZLF9eTVK+hEX3VSKoCfsohnUxBtMuyO31SNW7f3WQzH3WKtfRIStyQBP5HffWJPqsn7ZUeJ0eAqq0Nf9oCfkMQDKY5EPpavwXpP8Mi6uZ+ylhF7jHk0QjyHEMMIW+GE9S7Uc/vCLUF/IZebELExsRntZHhivJcjp5IbQE/m4QX5NEEVrLZyN1F2R78NsTTWaoCfs9jv+hJkKGksj3uydRs4Pd8NsDeHnb+NCTIbhsibSY1HPgpqiVJDF1nqAr4CZEJedZEmyVtcp+xXsBuspIJT2Yxm7HM0JtcFp+G9ZtRM4nMM0x6MqMas5tJVcCvky1Hs5TMpCyzrk1cU7eOAD/DYbIYQGAk6seCkbFsuxea/9BIqgJ+8mDVhcmXPqVpsp2/jXY6OkpA2IQ3k9usjQ+iWPrAJDdL+9TXGvqqbTk5VWQdWOKnpXFVVAX8QMokvdi9z3p9KxGAGuIcc0QZfBv2xLh8PZmgpw6NIE6OdiPODL0hSxMjg1MF/ADRihC8MnnTigwTL/EyPu3ak6kt4Lcpk69Oarff5IXMNZLqN8nqKFUBv75hT8s2yIoUu0n2Bmo28Jv3ZF8SS5YtybXniH4iJwiONEM3Gwr8wMas++9973t5w5TOpD2rgN9kMAZUpOa89cvAB+hbzieyBP6xCQxQZdQIo9nLvGezwNVJnV2nnmUBbjRVAb+JWGZW62DLrkSP2sngm3VtFrcZ25yTABj3cxAYAsvOgJYlcIyjSV3eYdIWHjEYrsUDfWHJWiz/astR6yhVAT+v32x9KyfURdu8izMD3Cyb4/AEMIoQrPUHuPZX4LCpu3ab/Oca7TT7n/E3SY8iMJYMo/51vixffjNMnuG5eGOZps2BAJ3oOgivbJ8LRMiIPiBDnhmz+9Vd1ohiyAY4Z1a+yaFVVAX8nDLy530UTeHV20fCO62j5yhTfse13Xu9K76Epi8ZgnCkZYzwhTzgI9kRLVg6aQtrz1VvfV41JGPZIkMtKqMTNlGye6KIEJ85QlY6cEg82zF9oA9lJgA1Z9WHmjzbvZyW2LGxI1QF/Nph2SG5IEt4RH/1w9RTT51XLehLkRH+6G8rGtTfNWRDIXeTIrUF/Jw7qzzYVrwkl7JFbB67oD85bfSKrdDX5MAeEGwF3cMPzisZJP+cb7aGHOKllTZATB1iNYy9TNq7h0kV8LO/VkuxN+yb4j3kSH+EHZMF1mfkmc1yXhGAWV6rH2NVFttJdtl0bRC4sedkktyRTzawfkJvM6gK+PGX3tJjdeHICxDIoCCOrdMvsjdh44G7PgS25Wjes8Jmsj1WMeGbZykcKPt5qIf/G0UNA35GgvFmWAgwwewMVQF/mVGYIHJnEAAbQ4gIE8PMiAZ5DqEkcCIHQocIq6Vczfx4TFsRv0zEoosumgGe4ioUTyfYkY2A4CMgFdFzUAgS8HGesAEfYEAJLVljkGP3OYrBUFurbadCy9/UQzTBAHSVqoBfW71HnSmxyAyIkRPOGYFXB30BJNRvxhlnzG0BeJa8kRdOCwMIkPWt856n76TJtQe/GHZ/GcUgRgmYM46MmfspnmOUsJwiJge2c7bG3TmgRlYYQ3XUZ5yR2JuA40ChAWVbS+CqgN99PHkE0Blj75bClcWSiWDAEAOi7xg/76Vf+KT/tUX/61MZA3zhOMmwOK7v1Rvg4ws9cFy2C1CWMwlA1Br+2WabLZfoRwZKNs2zOZixkZC+tOWy/lEfoMwJUV/t1c/q2xmgrQJ+POKYaBswoOecMmBnDwT1CWeIHJItx4EDh2SGGWbIfOxKSruZ1Bbwqzu7AOjIo74EbGQ9dFhWDU+AvP0b6IRzc801VwYf2RsOI9vJCeAc0ge7XXIeROL0jyzTC3ZQn5OX9gz1VAE/GRClskPqZmfJOeaY4ws7pH+Q97N9dBO40TuySQbVUYBCH9n6qKu/6kaHDLfZ04OzIoCjv+EoNJOqgD8CTX2j7trLTqgj/WRL8IGN4/Rri8L+smFV2XAOE1upbxEb4b2up99kvZFDPw0BfsYOYM0+++zZkBNAkW15fL29VAX8SEUJC0ZgJsVm7BgEJO1PSJDOwlzCzVDznm2SInpBnq+O2tEsGh/wW7fNe9WxwEnUxFBTYkaaIwBkGHF/GXxEmBhmwMjwAQ3gITqiZNpPMRgV7/W8iGApD0EtA1NnaHzAz5miCIADrymCPgiAJtSATGGU9Ck+uA+pG8eN4SNL2sQQ4hfPlzEDCoybNoYBYkwDJIPwDa+Ab316XqQMLESXZIFCcg6Bn/5htLxPtESGKSRedxT4RT6cYOTZ3gfEKbL6M3b0BXHu8IdeRSoeeb8+ZqT1JWOCJ2TDsxhW9YshHdkwcsABp4NAuTzeSt58vEpdnMNL/GNwPV8d8JJz4FrvCeeFodUm9eO4kjHtVo+ynLeX6oGfc0SWFO3hANN3siLKBy4cAdEV0NNPdAKQeBbw1K5GRkaNpraAH2hyqugOW0oeLXd1jG4JeMh3ONJkB7GJAJPekGW/RZDAHcgqs846a+5X4MPO4Bng9EwOnL0j9OOEqAr4OSjzzTdf1iGyDEzYZe9lb8gX8l5OBtm1z4QI17PYZf3lOXSbTeb8cfjpIFkmc54ty8nB9H6OYVvZt0aSd9UDP7tFDtWPA05X4A27JZuM2GTtwWPnBTTaJMihT/UyyqYLzgIzBX2cXFt7cwjpZHl+UFepIcDPM/U/MNXw6aabLitjeUJVe6kK+HlYmEWgpFgwzbsJjcpTJspAsBkFAAFwYlzXMcrDWCDnKYjGN4uqgN/7GOPwgrUjgIKRFaEBVsDvN8NG8Dk6PEIAAMwotfHmcLLwhKEAPJ7Nw3eN6ykXaibwc8IYf9FkmUSD9puPaIJxA4QMAIPF0Otrxh3wAEf1BoIEXgpbul4mAEBor/P6mTKZBIb0PyDiQHim6J5hYHwoX/08B/wEkGHs8MR9yPMZMkrIOOszjod3e379s4KqgJ8xFA1EWlJ/A1fPZ7QC0L2bfDIi7gHIiMOKP2SZ4WRsGHF/1YMRDb2LOSuyIwyrqJi8M0blvuIQ6CvkGo4E2TO8wMCQL8+36ZL7vSPkVXZNxKhfyKX36B/Pa0TEz8gykAweeUciWbLALnA41M09+oe8aY+I13g4vdbeSZnaAn76KYosE33Q9wBf+/CCjACBcCjJOH2il+yNoZvoS/pOnuedd958H8eJPNFD8s8+C5g4S+2JJttK9ev/cvaNjSNHHDaONArAd1x9XR+ZPQT4ZTQAn3r67V1+02H6xwEwtyt0cmIBv74gfxxTfcmJAvx4rI7qh1eif7pF3zlebAQ+0PGynaD/HDlBWz2xk5zBRpM6dhn4EaNGqDWC8GFCOUXfXqoCfkbvW9/6VvaWMJug6giCzgvktTJEAYDf+c53clQFMAg5gNBI1/ifsLmumVQF/PjBsFFE9QBwFIMxFSmvv37t87UR8VNgfJYSo+xS/fjK6FMuSs2BocD+92zK5l7g5j7XIEaFQW0G8IvGgCIFKZMNYkTNFF6/iVY4YwwSBVAXY4OURRTiOqBOGMkQHrmPUjD4nCEgpC1+MwJBQIksOIenDCOv23X1RhZfARuw9TzvFi3yqMk0R4TjEJviGFrCR/U0nlxFVcDvfvUXtakPXngWuaRD+sp79C0HAYjJ7ug/1zL4+OAeBto9Mj3qykgwit6JP5HVIEv6AiAASu8qp/rJk+eHbvqf4TLeGnURyYSxIm/qiTgwdEjfaY+/HA7va+84cZmqUv30FL/Itf4nM+SNM+ed+lMd1VX9BBqcS4bWcTI+KVNbwK8d2ut8maK/AAD9kA1Q6DoSRZJfjhIZ4UCwg2SYnQQaAgEOEdkhL7Jx+kzfkjFATLYmRFXA753ks2yv2TFA6F0cELLFgdF/6ste0zN80L+IfrNX7KbrtFm/kzfAzzEgEwH8dL3Zn1ZHVcAf/NVf2sme0zc6gJ90k5waxpBd1HfsM9nUFjpaJoGpIJZzU08cajahPkPQVeo08IsGyuOmZZJalL7uDGmoqEiUH+QYISF0zqlwCBqDJ10caRDpMdfFtZgcQq2ROpHBaTapH6NWP1Mcb3SwehFmisODFDWH1w1ctccxTg9ea3NEOSJBzox0GcADNt7D0fBOYCrCNE4dk67wsD3jeBMijp3IQzQS5F0m71DaegJ+AEM7ADEiyGW+cBzUGz/CoSA//tcmziDCH32Nd1XDSJ7pPXgV7VavsiwhaVFA7TkKOWF4wpHwnugL7dUXwWtp1Cqi4NpZRRwz45WiI1kQ/EKeyTgqjscEREZmoYUWylEZOcKvOEeW8cWz1BFQ6OdyvdTF+9Snvr6egydB2sdJ1Dfah3+cYjrCkNGnGGpifACWMV3GHdgwWlKzZLajpB74X090RJ/oazobRk89OGD6Sf+pq+EkdTVfR/83QsabTYaL6h1I+qnUE93WVnovwuXEkf3QCboeQzl44Dh+sQ90R5BAHvSl/g29jfNkqUqXqoicqEPZOSGP9KN8zLPpL+eSfdaX+kn/6Tv11S52jOwgck6OHdcOOqzQHfVWR88Np8N99XrdDNIuula/26u6kj+4xH7rO+3ilNE72Sdgju/4QKdgj76kc2Xyv2eUA+lmExtkXkKHgF8naSDPUYMJW7noXArIMNWfG19h8AiqyRs6PJ7tWQABsETBUOcZMv9TAO+j/G1dSxj9769Oqn9/o4p36WA84pVql+Pqhzfl+vmt7urtnGuiXfjL87X0jIcujUuA8MM9FCkUn2BSDs/02zPiGu/ubJ+Ui3sJvHQdoPIOx70/2lB/j/dri7q5xjPUT/E7/ndeUUf3ORY8CmPhr//junJb/C6fVxfHvFP/x3WK/lCn6If4G/V3j+K393LAgtf1z4o2MG4cojgW570r5M57/A558D51VRdFm/AUuIm4RUCu9d6om2uCV9ob/Rz18r/6ep8S/R/Fc1wfdQxeeJb7PFcdFX3gff7GvcDYxD+ZBPd6vvtCFtpTgmccHhFT/B/n1C/6RD3i2SEn6hQyFXVVj6hv+V2TUiFL5iQBAGBS7hv8V4IPUUJ+tFWb9bN+cK3z2hv3eZ7foY/BP/fhjWOuCR47H/wsv7OqeC9HQZqaDfAOx6P/439FHTwzZCvqHv0TdfRM98Zz1Mnx0IvoX8dDbsmC+ruvXhcbXdRFW2XAOGvRN47X64rCEZBJNPQm0yGjy7lVz3J76nXF8xxvdnuieD9nTV9yCuupTeDnqUkdiSp4DeGddbXwlEyGkELh4YqM45z3RCTPYDgmSnMPQ1lfjzhXf208o5H1ri/qLXW/zTbb5HSV91ZdN76ivurISEiNM7QRxTnX1j1tnWtE8WwRgjR0ff+Mr7SnXlXXtPdYuUzofKNLvE86T6o5jrV1XdXx8m9Fn0uJAtn23FdV2rpvQmV899AlqWdgHZmVzr5DkR6Vjo7/q64pHyuXCZ2fFAv+yXCwndK+7FDVdVWlM21tJA/ZHfVmh7Qh7Gp7SjPq3h0l+svwoVUt5fpE/eJY8INuSPWzB7EpT1xTvn9iFrhiON28qJjwXqYJjvH7a6ygkUVaR6V4iHFMqkQqkZfluHSPY9IwnBDHpW/973rnpU+Mzzoez3FteF5SoHG8GYVXqx3SU1Xn21O0WTuk7hS/Hau6trsKflIC7as631eLlBnFloqtOt+RQrajv6vOT8wiRSgd62/V+Y4Ucy04EFXnemshH9LDysTW5Y4WUS9nxfBd1fneWLTVBD3RftX5coE79JZ++NsIHWlWgY2cAJnpeprgGD+mNJqkPYx7xxgtUlHRj0gz1g8zigCfB2qiC0ekPInJmAnPyzVIKonnJsIQibc1SatRhEeMmvf2JiLMeN3MPRB6Ipm0yLtvUfvJ8sCqMf7eTADBOHl3TExrNBmnNxOfDegrpK0yrb3NjgsuBCodntzH+DdjMoIxiPpZ/WYOGzNhWI0zmSHK++QImAXuuJn6nBFkHNwY6c9+9rMvJpQZz7BeVcqK8PLgmknGz9TVOFdvIlFK/az+FlXP6m/R+KlqVn9vJ1lJ0X79rP6eQFWz+ns7aauht95mxwXTIv5JGvhN7BDBIxkB6yMtU7HUI44Do1imRDiNR1nyExv8WE5h2Y+xFx5cW7OzG0Ut4O9b1AL+jlML+HsWtYC/91CPAP4g52zcIIUfW5uadYkMD3AEYuMPs0f9H8v8bIZjiQXQ9wy/ZQPMTreCoNHDFi3g71vUAv6OUwv4exa1gL/3UI8Bful+gG1M39ph5230YYIQkua3GUQYXpG+TXxiP2cOQswBAP7WWNpYwg54NkKxlKKR1AL+vkUt4O84tYC/Z1EL+HsP9Qjgt+TAxzds4yg6jw1VALad0aT77YhkJ6ogqwNsgOJaEwFdC+gNG9gZSjG739pQEwAbLcwt4O9b1AL+jlML+HsWtYC/99AkD/xmwVo7botEW5Da1MQWpSJ6S8pszwr0bStaHrd3zvp3QI/sOuU+267a8tRQQDOpBfx9i1rA33FqAX/Pohbw9x7qERF/GFORu8l7ShyjSOUPRIyPLM1wLWei2dQC/r5FLeDvOLWAv2dRC/h7D/WoyX09iVrA37eoBfwdpxbw9yxqAX/voRbwN4lawN+3qAX8HacJAT/j1NuoBfw9i1rAX6K2gN/GOT4RaWc8Y+4mz3WUJiXg106rBso7AraXqoAfKNipLD63avMh8w584IVBaC+ZuOgjH1Yq2JjIVsT15JhzsY9Bo6gK+MmBfRV8NlO7FP+X51yEzJS/DGfIhcyQF1sbB5mX4XO5DE17yUcuPN/fiUFVwG8lij72JTt9bm6JPjOh1CoU/HFPd4CA/bnxekKOifrY1VL/+h6DVTHk2P4Zjaa2gP+uu+7KHzihG/gHKNsiekMfbOWNt/369WvKjqKNoraAn83zjZKyzdQen9mt2k8d0SV9VP5SZjOpCvjj0+HlL7VqS090bKqoO4HfxHUfqLMterOpYcDvE4mWxjH+8SGZznw2sQr4KYuKeh5ADeMFhCh5CD4gcb58r/kAjIJVADG27y/DVr7OszCc4vmNdLhNgTpj9KqA33N9E9v+Ar5CyADYdZCCO6euAFvdygZa3fHcNeY5MM4rrLBC3oeAQY85Du5znfb5EpZrbBvsmbG/QVdJHeqB30RJky8Zadt62lPBTot77bVXri/Sr5ZQ+nBFkHr6sM1XvvKV/DeI8DvmQzWI46U9ZXkr9yvyjWzPj898AlVygzdWbXTGeesIVQG/TaR8WZFjF06xTaUAGx1RNyBru1+kvuSP7CC8Y3hcZ4tkz9YO510bRAfwp2x8tZ8TFFEzAOGMe6ZnBDj6zRGpv877yChjv+KKK+bfiBx5bhgn9fBbP3RUxqqA3yTcBRdcMO+zQZZ8Gc0unHbbRFHfcHbpsEm/8dEgdSUD6lPuC3x0X/DIc8o8tES4/H+zyDuqgN+kZTLv415BViw5Rp+QOpPlsKtsLFsSS5Hph75p1pa6VcBvK3VfDS3bev1Bn8kUPUX+sh3uDTsXfUhW6wM9MlXu55jfRRfoSHdtGzw+4Fe3+GZJUNgl9dTekEF943jwo570q69ezjXXXF/ipX1mqr6g11VS54YAv2VzCy+8cF4mB/A6uyVuFfDz6P/0pz/lyMnnaX0BiZFhoKzl904d4zhgXXvttfNafYxmQDgjogfRNoECKP5XeMyUxbOVlVZaKW8FTOh8F+BHP/pRFu4AsPZSFfBjtmhYNMMAEQ489GxGTn3UH2BbmshIAHZtdlzdACfDQbkIhS8sqas2abe2iiYJpH0P8Me1zjUi+q8CfvtY2zJZFgJpp/raVyGMBOdJfRirIO32hbZf/OIXeWUGgccX9+I7hWNs7LqIN54HKBkK31xgLLWL80T+nAcm5MCng/GKM4InZIRsNYuqgJ/xE+lrk+O+TT7HHHNk+da/ZJyhxxM6JSNg2Snw8klnEZ16c6i1TWbENXTBihXywaHAL/zBQ04FkA4n3PcpPJuukBURGp6oL575rY4AnyNpcyy840ioI8C1EgYgAxjXeZ96eRYA43DZM8M22h2heuDnCHr2mmuumYEYMfDaIyPEufNe1+ABvmnv1FNPndsq2mc46QuZJ1vkleyRIcfIjHpqI2cBeS6dp4/NpraAX7tmmWWWtPrqq+f/8V9//vjHP85fH+XMhCyzA/rdnv8+AUvX2Q/9SH70eaxmaiRVAT+nxFbobJEAAK/Vj6yxwT5VjfQhmbZCi50ln9pnXxYyxSFmP8gA2dQWMq0vySDZ1O+K59N/dqDZVAX8+lA2VV+EbdV2tnznnXfOwaLjin6Q+SN/6i6rU7+iDCY5zu7TI045B8HzInvOVpT53lVqKPDrJB3CONlPv60U1fioCvh90OKXv/xlFgjGhper0phEGXxf2D2UHlAy/KIGhlfEdcghh+TOcw9DM/fcc2eB1Xl+M74MMqBmSJZccskMGiI1RtjzO0pVwK8zN9lkkzTjjDNm40ZACL82cUrUhXcHyHjR6kuxGSjgri2i6DD22kq48IXiEB7tC0FlkKXMOTZ4xth1laqAX930ha2U7ZXAiOl/QBjEIKhD+RijLqIDapScgWNcLNH0PEYDD+266Lg2MhKMzEILLZSNiz7EB4bB+zk6eMuD5vRxNvCPDDDwzaIq4Ac8ZFK/6AfOEcPA6AMudWSk1Y1TxgiSdaCOf+RStKuNjPy0006bZcJv5+NLlgwq4+IY3fPRKs8nUxxCIIJX9rXgnOOHqIlsMV5kXt9wSskn/cFPsukZwITDpV8Ak/q7lm6o8zzzzJPf31HgrAd+zrx+154qohfky7s4IwCAXNEjckIvRE0BFLJQ+oTMcOI5lngNVETTDDbd8Fs2ptx3zaK2gB/f6Q2ecnJkhOiEfmSLtI08uc9+JhwY+qJv9B/dYHs9m95rc6OpCvj1yZRTTpl3UdUnZGb66afPckc2yCNik8jMPvvsk3VXH/o08cwzz5zlmJPJDpAhDp3/tZUus3Fknn3EC3rsu/ee0WyqAn4Oh/pxNPFDvfzmuOgvMiWgpPvkj0yTMf2lb8giBwexp/gA4LXR/TI79qYhE7PNNlt+Ppll9xpFDQN+qcGyJ8N7YWyige2ltoCfQgQRdsxihCgE8v90002XwY2HFEbLJ2QJDgOnM3SA6wggx4DxO/roozPQyhBgOiaL8nUqTy3STR2htlL9jKfoItL3+IZHhEY9OQc8eIqtrpQ8Ur8UG08ZNsCvfngg4uakUBKF0okQGETChgCM1HNXjVsV8KsvxWeogIq6MLyAnccrsgdyQJjHH+R8CD15YhgAJKAj9OYJUBzngeEiiyySowUGXL9qO+ONz4ZLfIth8sknz/IhYgSmeI2Hnhcp9WZQFfAzDqIhbSPTIn59zVCTZzxhGIER51J/cwzwMb5HITLSDkAdMsrRcn84iYw8WWZQyYh2AjL9769n04spppgiOw/OkymGibHmWJFzjpK/gJ8eOkeHABKDy4BxXnzvwnOjvzganaF64Ccn9JFhLBN+MXr4pz+1VT0ABfkiE6L4yHREtoAOAR/XRjaCXpAFBo/8cDLILue7O6gt4BfAcILJDH6Sd5k7vGeLgBwdJh9Anozoe+cZcM4Xm8A+OEeWOmp7J0RVwK//1Ie8AG22kk5qB94riF1g+8ihOsuCAToOJ6I75JVccjL1s+yTvtGnHCH2kc7gIXyJ4admUlvADzPUEZ/nnXferKvqSx8QZ5stJl9zzjln7l9txxvyG3bCuD7nhw57ngBXUIoPAme2yz0yOo38ImrDgJ9QUCQV5JEZ42U0GgH8FJ7nQ/g5ARSZoPCO/EWAUwcwwBSLAdRhES2KunQAwGeMCZdnARVGDahwXAgvgyfaJOSipM5kLqqAH7MZIwJu0hTDx7MjJICMsyLqMUyibe7HU0DO8In4CQ8lYCB1HACQhiWIHAKCNs000+Q2MYzBH84MwWwG8AMWkbb6lknKirKKVHjEold9GQT4Azg4CrPOOmuWG/1AUYAiMHdeH3AcKILfwFL/6VceMQeHXIj+8dG9ImZGkzx4Nx40i6qAX9sY4fpxPf1FrvS7a/RdGBD80Ta/yar2AisAFzJKphhMBgK/GA1Rk4ifsQlHA09mnHHGDAb6XsET78YTzwYmnuddzgFRzhhnwzv0KXl1Pf4Be0MQ3sEBAFAApzNUD/xIvfUV3aUHHEUODRnSr/F9Do48PTb8oH50hL1hJ/QDGdFOugJogI7sgLZpN9tFVwQB+ogsdge1Bfz0nK1iDyabbLKs/xy8cE7IPl1iBwCB83jnPLvIUQS2ZAXY0rdGUxXwC6bwOebaIDIoM0OmyJD+Yvf0n+OcS+BJV92LOAZsGceWg6tdQNF9HE5ywJ6Rf8GZ+9i7ZlMV8LM7HGiOGr2XLaYLdIV8OkYeybHshb7xP5uvbzwvSNCmr/Qx3TPESQdiaBlf2PlGU8OAH9BFtMJAUNSYQNQRqgJ+lTOOhzGMDODhMWIiJiOeJkEDGCI+AiYiEpm4jzcFzEVO6uY6AshwURZAoQ3GW3QioRLJUjbeXEcNQxXwU3oCAKC8nxHFL+DEe6e4BC3SjwyVemiDaD0ETLsYc0ZAOwGv8wyC+QOAg9F0LNJD2uA9XY0CqoAfTzla9WO82ktwKYO0LEUpywThNg6obsARMGkPwifAxhjqP0Mc2qfdnARRof7DG4okPUr+TPTEH/wNUNL3+BK8aAZVAb+26a/6SW8MobbihShbGzkD5Fa2Sl2BOSAmE0Afvzk5/jKOruNg4xM+ALT4672ML1BX8IuBBhT6j95wNvCDDOKPqAoYiTJlbMg++eGIhCOgcMIUwMtwcDDVoTNUBfzkXx0YfnbEX5M9OeTklwzQc3XAI3aCPAJ3+ua8vtfvjCkDp95kiKx4ZgAG/WLAy/NOmk1tAb9+Vg/ZLP2ozUgfyGqScW0SLWoL2fYcgQB77Bptk81wXdjFRlIV8NN5ul/GAm2JlDw5pKvsKDkhW/pFVgawk3/EeaH72k/uyVe0la54DzlkV8kwhzm+zNpMqgJ+9ple4XXYJTqnH9gnusmuCzQNgQsy6Znr6XhbdogTq61kJIiT09FJs+2hTgM/5QS+ZQIqjIzSWQJihKOcXmcUeU0UuDxpkLCVoynvd433B8BhGmD33KCop2uDeKBxj3ZhDNJW6aU4116SlmHUGO0yEXhGldFRJ+1hzNUz+OldDB0DT7EpjGjGmKUoTptd628oHMABfkAlwMczgj/eS4i6SsCaA1V2zNRXPcoGoUy8dO2t4qE6RWrW76hjTHBxj75yv+u0CXmXyNPxIH0YbReBMBAMBU+72UueGDHRZZm0pV5HEMXWDg6T+SScaOR/bVJ39+Kp+7UpeOxv/Eb6gxzjQ/l9ZJbhjes4tArC1+g/fJHmjjQiOfQc79QH+Bx9gfAYP8kmwuOqNraH6CW5riLPVy9ZkTIxjmRcW/AQL9TNdfhGRuhcvQ3CB0aYPiNywSkDTMCmOwnoixrLhIdh4PE/dEkbI+jQx3TctQoelHmPZ/pGHzWDgDMHowxM6hkyFkReoi3kTJ31DZuqbaHv2hX6rA/dF8Q2us+1jods62/knd2RpdFW2WQ2rEx4T1fZcnXTDkNSMsjsAOc9hi4QZ2FCfaM99bxsFnmXjAtsrac2gZ+x4JHyXHSONKNC2SimUj7e3uJ+3jig44l4BqPGEeAp+S01Es8Gmm2932/HZBDcU65T/XUMiWf5q7gnjhNACud3vGdCxXsMeWiHcaioi+LZvMd4v+LdrnHOe8KQExQesXSudBehEs3Gtd4T9XZMO5Woe5k/0aaoR2eKZwITnjcjEM+uf1e5aEuZ1/Xnoy1++1v+7ZziXjzzjmiv8+V+9R7n/HUe/1yj/1zX0T5sb/FMRXYCgMcxf6Nt9e+N47x7QxnAT93VVXvU1f+uUaJN0fZyWz1L+8r8cdz/jvvfOz3HtfE7nuVdrvNu/8d18Y6Qsejb+uujLdG29hT3+SsqZEf8H8cU7/Z8AK4d8W5/Q5bUKdqgDuX2+x1yEe+LevuriDpNEJMtiOuaXbSL82EOiyiyzLfgu7pqQ/SV3+pX1XbX+Bv8a6ase5eMC6efDdAWx9Ur6hDXRt2iTuqsTtFGf52Le+uf0562+u26eGczijaywTKV5iN4b5zz/tADv/2VgTEcIUBbfvnlcz+ro/va0zehc22db1TxHtkSmRXOSj21CfyicZFojCNKUzeiAH2VkeaOZ/OeytfU/z8pFvXmJUo9G7/Rrqrrxle003MIk3S55/ntWROLB94rnW1YRH0a2fc9teCJwsnjEMWx+uvKJc5zFoxd4qMUav11vbUEz0wiNOwQ/1dd24ziXfguoKBT3cV776E/0sGGG7276rpJsbA7UviGWQxJ9AV5jf4SfMGl8bWZTEV/cuo4tZyFSdFGaodgg0NTNVwywTF+aRwpmkYW3gigkyKJY9ItUo/SE+VrJ+UitcN54cFVnW9viUkeyO+qa7qzSNeJkrSv6nxfLTJUjITUd9X5qhJpy54k140sxvg5P1Xnml1Cr9iWqvPNKuRDulypOj8pF8MTgEz6vep8byxS+ubHiNirzpcLPQ6dRpOyXutDDlynJveVx2QaRVIhgL88S7QnEh4Bfumd3kSERt83cmlJbyDDXsbMWtR+kkYVdfclEsAA/fqJsD2BTPQE/ACtr5C2mrjX2+y4uRmdBv5mTEQw/lM/q78nkoxF/az+3kAilvpZ/S2qntXfovFT1az+3k4yDFLC9bP6ewJVzerv7aSt9bP6ewN1elZ/C/jHTy3g71tUBfx4ZBIkQy/CM9MemQFsRnDMsu+r1AL+nkUt4O891AL+JlEL+PsWVQG//Qni+xXWWNt0haJZGmRiWyz16avUAv6eRS3g7z3UAv4mUQv4+xZVAb8Nd+w8Zgc2YG+jD06A9dZm1VrTa3mNcVM8tVQq9huwDMg6c0uonHecEXKfWeHAozzWaqWNYzHvhh5RavUxNutZ6hfrqycFagF/z6IW8PceagF/k6gF/H2LqoDfrmu2lbUds+Vbtp51TexohoeWrtrhy3lrfy1v4wz44FXs4md3OTOqLRe0KYidGe1Vbi16vI8TYa8Hy4qAu934LNcxa961+szuiXZ8o/STArWAv2dRC/h7D7WAv0nUAv6+RVXAbztPX4kUoQNcHzOx/4UtPKX9jfnbztTGTKJ30TzAF53LDngWObKHuWN2r7QHvaVoZsP7v7y6IrasVhf3UGrfdLD3uX0XbINqp0D6OylQC/h7FrWAv/dQC/ibRC3g71tUBfz2MBd5B1k+44NC5Bsg2wvBcICNUZBlXj4gI7LnAATwcxJE73YEky1AgN9Hecogbttan/10vb3Mpf85G97F2TCvQBaiu7eobYtawN+zqAX8vYdawN8kagF/36Iq4Je2l8YX7ceXygCytL3fIn6p/Pgwkft9wUyq3xbVhgFkBHxBzi5gQNvHTnxgx9agsgDl9yEfovF1N/WxmYjhANeK+Ndff/1cp0llnL8F/D2LWsDfe6gF/E2iFvD3LaoCfgAuyjZ+b1zeeDudMVHPNxzsnmWvbBvZIBE742oyoHkBJvsBe9/N9xEgE/fsIiYjIHtQBeDkzbkYx/cBEdvT+tIZh6H+ozcTk1rA37OoBfy9h1rA3yRqAX/foirg7yyZ0S+ylx3wiVZDBjHbvzdRC/h7FrWAv/dQC/ibRC3g71vUSOA3ec/W1ZbxeW58xrS3UQv4exa1gL/3UI8Bfh0gTanYLz7IBCaf76zaBc0kprhWxOR75+ruOwDSpuUP4DSa+hLw4yOexuc39UczvuNQRd6tLvjsQxrGzSeGYaoCfml23+tWL0UKP1LwVURWySxwIM/+N9vf7yqHwg6A9KT8YZCeRFXA75vr9jwgR/ZHDzkid/p5UpmfgPedcchawN+zqAX8Jepu4Pfbp2CtYTbr2SxoRtXSJuuhzVo2CSrAyDnjqWY3AyNG06cV3f/Xv/41fwPdpCpGplnUl4CfPEhJL7roork/8N1fY9zNJvVYdtll85I1smHNuhQ5J687qQr4TahbZJFF0hprrJHWXXfdzBez+NtK2/uS4yabbJJlhrG58847s55tvfXWlcbWPAF90VM/mlIF/Iceemiab7758qoG+w6Y5EiHOXRWNvhW+aRA6qJvOkot4O9Z1AL+EnU38FuKZF2yCICxsCkKg7HOOuvkNdKOGxM1O1oECPRNiJpmmmnSww8/nJ8R2QLfSZ5tttnSySefnI83i/oS8OM/4HWcYXOO0bYWXaQbE8qsOQdusgMMuP3r9QfDjoD17bffnlPcDAzHzJI3Y97Is/22812QezlyvhcuWuYMWtLGURQJi7K9w3eowxkQSRN4S+J8VY/MdTX7UwX8JtSRy/jSJEdo9tlnT/369fvif7KKD5xVn6S2Nl997PEvcwJgOAP4NWTIkPwFQDP88cYxKwQoMT6rg7biYU9wBqqA3z4EG2+8cY7sOUjkiOPD1pAL/UkmTIrECwCKd4gseB5+3nPPPV8cd60vft58881Zfssko2BnQzYHL71XFtGES88xMVLWBonwLbkE3Jw4kyw7Si3g71nUAv4SdTfwU0RAiiintcmM5x//+MdsDJHOESVoEAMIdEV/MWMa6cTNNtssGxYK2EzqS8APoPSFzWkAkEiVo2Z7WsvYLCFDQAmIiVQ5b0DR8jOZGMacM2bp2k477ZRWX3317NQxvPpR3wFGy92AYhCn4ze/+c0X69uRWe3eDyg4h5tuumnOSNjARr/YKMczvUdELlPQ1RRyFfDHpjvex9ibkW/tPUAZNGhQ5pmJe9qET5wW2SsOgeP0gNPE2bHJj7oCQTv0iYydl8kCkGR/ueWWy5kv/OAITepUBfwycRw32RIrG/APwJMrfUi2dt555/wNBLywtwG5MTRAZvQ1h3OWWWZJQ4cOzbKAL/hkWaPVFTH8xzE84YQT8nMtufQu7wXK9lvghJBR2StOo/70DH0z+eST5/87Si3g71nUAv4SdTfwB4l0FlpooaysFJ0BjHQyj97a6BBKgEDhy4DA459//vmzwUGMtSVOOrbR35fvS8AvCuOMSdECdoaSARaRinqBFmLkpbwBJOPuC3WMMMNrvbkCBB2TGXCNCEvUx1Ay8NLm5fkDAfxAM4iBAoSisg033DDLhPoCeE4IZyAyPoAAAHd1TkIV8B922GFp1llnzQCy2GKLpR/+8IfZ+TFmL4Ut7Y/IsGESOoV3+GLN/RFHHJHPIzzyoR9tkSXAk8gG0EcASN4Ai+uA06ROVcBPVmTrDNfg27zzzpu/h44nQJcO61PnyQlZJBNAGO+AObnjGMjycPA4XIgTQFY4kEEcJB9Tcg3HSmCgj/QX3eWQ6ht14BgIJGRTOBmx/0JHqAX8PYtawF+i7gZ+ykLxbDfKI6fc0rqLL754NrgIaIgcw/BSfmAUwK8DbYgi6ovJUAw1RXcceDWS+lrEz8kSNcnAMKzADZ9FZyIz5IMz5liI4BjrSIGLxmxvywkLwNL/+lvUps8ZXff6XaYA/sj8IM8iC4BAnZCIHkjqf++2pz0CPJwNk7W6QlXALxUsYiRbwN1wiHX52q0tAAfJaJFl4AVQXCtbgh9B/udIIKDhOsBvZ0CyxtEJXSDPeDqpUxXw6yMyYOiF3nPQAC8DpT8BvzbHcAmQFuFrs3sRfsj4yBaZ/wGwkJS/TJOhEEQm3McGnHTSSTnbQlbViWPm+wccQo7hMccckwMNjgDitLWAv/dTC/hL1N3AL9IXLYnoGX5OAAUEKgw5QGFUdVAQ48mYDh48OP8PqBgB2YIgjafYxku7OsZbT30J+I2tAuZ6Iw74GUzRFyPMEROBMXrG5UXfDDiDCgSkdmeYYYacvTFGLzJnnKVZp5566hyFhbMQZAa4qFqkyHhLFf/qV7/KzgWQIAPeATTV0divSJpjIMpznlzEeHBnqQr4tc/X+cgZkqaeaaaZsu4o6iMKBSJATXZDloITA3jKEb//DaUgTgugNxRi2Ap/6IYhBOR5+DepU1vAD+gBPh7FkBEHXpbDeLxsErlCbAE5MfyDB5wpbTe/x6ZHnC+ZHvKAhzJOkd3hcJFDz5MdkE2UWWFH9IcsoGEUMmKekQwE+TJ0Y0iKg9lRagF/z6IW8JeoO4EfeBjXp3AMqXFZXrqOANjGOiku8I+xO6SOOiwmgvHeGXpDBN1BfQn4GVAGuBx1BzGcIlOpWWl1Ro8RZdwZeZEpxw7Z4c5QjK/SGSKQSUCALTIK9cSIuw/wewdjTJjDkZPKdY7MAAIEcKwMUR/AwgloBvADKRFpAD8C9Hbkozvk3BwHgG7WOj46RnYAUdQX+Z8jgwyDmJioneYx0APOUkySNIeiJwBLFfCrN10X9XPsOer4AoS1kUzoU9E7knpnwOg7vupX/c35I48cR5kTcx84UjHBE5ERzpJ3uc+79CG++2syH7nghHA49REbFFswl/unvdTbgN/v+qXUeN4V5wCP8L2sSxOLWsBfou6O+AkAB4BAAHdjbCEUjndFyJpFfQn420Nlp0yqFeBK05ePM76iVccCuBlcx0S4AWxVRDbKclGmkBnkuRwFkThDL8LmEHY141MF/G1R+V1kd1IwcBODqoAf4Yf+ij5rDzFghmxskRwrA8rzdvCZragi7ynL4fhI37X32irqbcDPcZKJkc0Kol9V/dpekpXjqJcd5olFLeAvUXcDf0+kFvC3TcAcUIoMymRuADAoEznwPsagUeSZhoyMqTNmjQDejgB/i2rUFvB3hgCqdD/QoXeyBJMi9Tbg139f+cpX8jBe8Ny8FsNQiLNuCEtmJnQj5rxwGmSv6glvDK/IZsmasaXI37A75uR4jkyudwgmZIHUQaaHLMSQDpwCcPgewQMbIPsri8ZpacvJaAF/icYH/NKO5XRaR6m3A7+0GOEltIrlWoS3rWikikQcngOETXKsilYpmVRlV6KTKqoCfu+iiIZVrHmekFMQUVNH2jypUxXwc2z0T70zgFddHVpoD4lkyQCwmRSpCvgZJPJDjpQJAThZ0kZ/q/RgUqO2gF8b6u1p6BWdqyI6FG3vDqoCfitMLFE1adJQm3OG8AyrABXzKwyzyMYYjuX0mz/huPkW5tfEsE2Q+2aeeeY8CdcwnOvcZ1jXsCEy/GuYzHwLc39keWTvPFc9TAx2rfsMzaiTIWH1EGDIDFk1JDOkDiaHV9mj7gJ+9mBCdrOR1FDgN1arA0y2MW4J0DpKkxLwS0VTUs5MR6kK+Cm98UYCaqKRYna2scuOKC8PVbTKizbJiXDXkz4yDhmTGxtFVcBvHN9YvXZZhsbjN/O6rdS8pVnGShu9hHJiUhXwm9Bo0qlNgoJEOGaJk/Fmk82rrCKwBn5SpCrgxxfDQJGuJ0uMcv04chA7YV6HVTzmmQCCSZnaAn6TT6XMyxGwaLS8KqGeYvJiRMTNpirg79evX15eCbxNjtR/Jlda8aDvzMlyPRtHFzyDo2B4Tb3ZP2l9NkHf0SPvsDTYEl3HRf/uM+8nVv2Y4GoVBgA3QZOT6LfPYMsmqAc9M/nXu9hiEz+nnXba7ICoI8eCHTPkx1GocrC6A/hlK9hNE9XZ7O6wiw0Dfh1EYc3M9jBriXVAR6kK+HlDJvWY2UsAIoVjZr93ADcKpTFSPBQiOlHH8g6likNgPY9SxQ5nPD2CI/Xj2lAkHqO1veXxq/ZSFfCrn7FlXilFUYCCtolstIcxVLfgLa/edXjqOsdjUxK8MumL1y8ysCkMxdFOmQTXMJqOcWIaQVXA79m8bkqpL9TfjHXLpPBXZoOx0w4Ojj6i/LGjn3uk/brT4200VQE/JZYGFZlE5oVMORYTFckJECAH5AN5Blk3CTIi3jhG/mURyCx+SWs67l7y41nk2nnPZNBCfhlBE/8YVP1AThzDf4Cjn+qd+WZSFfBzGDmQdJhjK6JkzM3KR7JK2gjokTYDFClhxtx8EBnHcBT85YBOKtmAtoDf8k5yEZsCuY7+OIYHiP5Lm8ewF8PNMQgH20ZPZfvVaKoCfhMfReWI3V5ggQXy3gvk2woedUTqZjhAW4A9vEAm8uo3tlI0bp6P55jTExkzGQI8o0+uR2yg57BFHAxkIq2gU5+zMepFPyzRNOGT7nFOOIgmjsogILZHXdnRemoL+NlB8qtedCgIbukD7aNPgUNwqqpv9DOZ1y59S5Y5MEHsu3c0mhoG/DpO6sRMaVGGh7blpY+PqoCfYbKTFs8ScOpEdfC/zT6kkhgJDKQsUkMiSgaOIDC87uPxMRSusUSIc0IApH4sKSNAFJAAMizOTTXVVDly62haugr4CYH3WjKknYRD0Qk63SYs2kTopaY4OFJmNi7RBkplyZprrV1m7HjUnmUmtOe6n2dMGAmR2fPe6TfHoKtUBfwMkqVU2otPHCv148HKAnF01JMzQKg5BnjMYHEUKbA+kQGpGvPrCVQF/IwYIwh8GQGGTN/OM888uV/JLJkjv8COvDF0DJJoxDk8xBPH8Me1omHG329bAMu24D25j2fJuAH+SG0ybgwhOREJcQgdl6JdcMEFc9RDTgJgu4OqgF+alq5ywskYoKHjgJKREhVL2QoyRJscdpEfmRJl4gUexHO1i0PKwE4K1Bbw03egpH2uka2h99NPP31exeB6MqH/2Cp2gBPnGB4AJzoH5Bwjj42mKuCPCbJsGOeKbf7617+eQZYdovv24tAv6qXP2QpBgOewa+QelrDNsofay0mQMSYDeKKtsqWOs2N0g02DQ/of0QG/3ed6uiaLMOOMM2aHw0oY9pz94mxwJOgrcMXT9gI/+6ZtsIXdot90WT+QXfqp3XRLP8ow6Bt9p94c8yDv1251hnsCKPVhZx1n072DDehM5rktahjw6xTbWDJQAJYT0JlIuQr4Ga055pgjGwMgQyGkb0S00nyiIgJoPTeQljaTKgL+6hGGUSMpjOtM7nKPaIIRZqApl3cEiGEMY1n26NpLVcBPiCi49zPShIE3GwbQenfvIhjaxptmuNULIFAw7WU43M9IA018dg4w4B2wFYVTkhjnonQcsq5SFfBb5kQJLaGiDOqi3sCJ00Y2GGZKSRkJtnZLQTNUohZGXN27Mj9kYlIV8It6OKPaLRpxjXYzdoCZrBmqYejIhY2IGFYOkkiFs4BXDI90pglTjBdeAz96IMIlG55Nnt3P+WOMOIjklxOrH+isFCm500fkhNx7BydT39CP7qIq4MczYKfe6sZQ4x0d55RLC2svntFtURTZptuiJo4lfWaAySXQiIh5UqC2gN+whr7Ubm3BF44Y/fZbBkTf6F/9RI70JWAQAAAZ94uEyY/jHQ1WJkRVwC+ypt8R2cawH5stZc0ZYxMALdsONAWH7LhsoCGAemeTQ+oa18pk0hEBEtvAsdC3+pod8n5/ETtIxt3nGe4DxiZ8AmM6JwAh92RCP+ARXeMIRxvKVAX8HARZA3XnWHBM1QEfADtZNTFRthgGCL44N/BE39DP+r7BK/3tHvXS35x+Tgu88FxtbRQ1DPgxmbcSx6ROHOuo8FUBP+UGxkE6nhEk/Dw7ZKyLwaD4Ii0gyKAxGoSFsogmeP+YyTkgXMZWrP8FktKIojKZAF6a+wFsZ+YqVAE/ZhNAQlBP3sewUYbwHDkujDsDhniM2s3oEQpKyLP0Doqmbf5Xf0rAIBIiBGABfxmYOkNVwB/DCoy2/uPl8k5FAHjAmOkTUZo6hcPCyaHQPG9tJzONyEpMDKoCfv2sDyMaI2sMhn4QMZBrfMA38irjw7gD9ngOPnOkbfzDoJJvfckBJJuMD2KAGAfPIjucKcDv+XjMIcZ779Uf9IEB9JeDgegTh7q7qAr4AQReMPKGOjg86sTmsAGxcyNd5SgzXuqN//jNgNM9jg0HS1uB0aRCbQE/nVV3zps+YbM4eexFjFHrV0CqbcAU8AMRNo7TzXkAPiJEQwZd1fV6qgJ+9r3KxscxdWBTyxmX8vUTuh+Vr/E8djTaVj5X/o3Kv90Tw21xXZwv/66nKuAPBwTv2WN7j9BR8ifbhAReZI++Ci7Jomv0kWAshp4831BdZMdhJjsgWIOlrqfv5WGDRlDDgJ8SS5cQXt4Ob1yU11GqAn5gYKcs4CgKBiC8XEDBw0XhYRFMxoHwUyIRF6DhITovtQzkGULGkmJhAK8MEGkTQ8IZoJzSbwxkR8cI20r1AwBeH+WnuOovFUZhRcm8VWkd6VjnRYIEym8pYoLmtyhSvTkGFBLoa7f2ETQg5FzwB+jwdrtqDKqAXxtNwNE/ZeIZM1KKtDO+ug5fHBOJag8HTFsYr9iWtqdRFfBz1MghWTBUJS3PSdJXlFnULjKnfJG2J3PkmjwARQ4RfpN5uuW855JP/JQpQWQHMNIVzwWYZFzEK/MC9F3jXUBDdoFs0NkYI/beiJ66g6qAn6ED/GGkyQXdp6tAj9zjAZ3QJsN52s0e4AtHC3GAvvWtb2V9aMuoTwxqC/iBhGypSPX73/9+7m+RI0dev7MHHD1Rn2PazE66Dw84eNrOmQQyzcjcVAF/b6cq4Mdjjrh+4Zz6IBQ7Jghl1wSM5JMdjuEYjrrgTN+EziKA7zx74LlkX+QfeMN5d7zR1DDgR4SZ0VFxRieUtyNUBfwqN2MRpXs2ICR8GMNISdMggARQGA6emMhKpEyZgB6jAMBdJ83jOa7TqSJs6UFeFoYAMIZcpMBIa2tHhb0K+NXZu6Xhoi3q5jpKBbjxTNpH2xhkHS+9yYOXUuItxgxmDgMFB8La4bkEC++9Hzi4BokeZAq6agSrgB+feLPG7coEBCmGdjJKok2AhrexGxpDph/0G9DvTal+DifAQvpJwT/X6QuePsAnYwwHYKNb5J18iAIZCbLHiebEclRlTwwTkJcYTnOek+FZjAy5N2Tkfjx13jNlDbyHvOsvsh0RsfqVxx+bTVXAL3qVxQh9ozNkK+RcupZ9kb2LzBJ5NGyE35Hh8tfwYHzSeVKhtoCfPrMBhlzoOj4gOqyfGH96xBbQI8f1qf6VkaTnUv2RUSQfjaYW8NfIUBP90R90SX8IxmShOAP0k+5y9k3qk3GTIYi+qV9lE3aAHZQNjAxcM6nTwE/wRKf1xPB1RTCAHZAqOxUqJxox3lNOGVWRd9e/vz7VhFzDCLeHOhrtI6DGqMXM9XqK9NL4gJjHDzAV/OZNjs8wd6RNnSX1VRd1ay/hPUFrizyzamJNTyIAzCh2lLS9ahIsParqyyqdC3LP+M7rB7owqRCDGADXERpfG5HxUdkuRnh8+jWxSOQHRDtKgoIJ6Xcz9YgdFohMijxtFmmrwExwWE/1cshBi6BTIAPMYVbQhCa7d6cNZCs4ylVZ+TaBn9HnmfDCRTSAOgovQgqx/nh7ikiVd8878uxIx/OApbRcI1qKZzsf1/jfX++O9yvOx7G4tnws7nXM3/gdxxkmJc61p7jfMIP0jQi3/tmeh09R4n311/nLS5QClgbnRXIm4tryPX5Xtanqd1eKjIXIU/vKzxvf80MmXBPX+Vt1vnxfTynaI2KXXtY/cTzaG7+DP+XfVbwpH4//66+tf079+TjX1jPL5+P+8rXNLt5Dr2Uoyu9Uh3Kd4trQQee0oayTUW9/tdFwmmhLVOb/uG5iF3WUzSAnIsVyO6MN4/ut3doTbY2/8Qw8KctHI8v/b+9eWi27qjYAI3ZEAyaRlCYREpOUSSUxF3OpXKqKxBvYENSKkGgQG2kExFYwoqgI4gVtKOkI5Q+wpWjDnk3Rpv4CG/6RfDxT33wzy7nO3muftfY5a+85YHH2Wdc5xxxjvGOMefNuNl+XaQa1te47pEMd1VW2GC7VdcbjyKHz/upWMj7HYDyDLUX+kb9NbbNk2w0P5eWcGBdn0O+QRoFfalBalocjpSTlNseR1LZ3i2ylRJz3DWl71w2a8Nd5140OzfkMLKuv5VzucejL9zf3zn34Nu9YulVKL+WdeujrV1Z/pSxlD+bk99QDL0VpBEb90j7HfuALh4gzpJ1a9/Tj3QeeMYrAudbbOQ5tIDXraF0/q0M9ddNJzzO8S9qguQ+81LUk28cGzN1m5/FQR6l6swL01bPDrftyBHOM08CvGn/O06Eeph8KrtNVWNOJqX5GbonUBOaJmvSj1KR/T2pCOjX9y9IVHASekudqkvZWqZRR+k8fv74yB+HdJYW/LXGOgIHGPySSLtb26Rfu9B8iX/r5Om1PxueIPo6NgMgSA7aWJmlh4xOWtJvnjdTVmK9Ds+Nmj8HTyX38rcF9cxAPaTi4jwdv9LPBSwbDGc0sEs5cfv0pRr0yvkh/jIEX9nb3PmTKmP4XoyZvu+22MkJ2yT6V1uC+QyAO1XBwX6f24L5OJ1NrcN+hE8e5NbhvDdQH9x0OzTqqfw5qAb+pYEbzIoBjqqC+c1NeeC0GYBgtqT+cYimbuZAWAJFaq4lxNqUsy14uRYcK/AYXdeD/X+rAP5068K+LOvAfDq0C+GsSuRuxa96+KF/KHnmGI2CkpTS0KRL+Z1xCyi1DoM8GMdIU0TH3SNUO/MdFHfinUwf+dVEH/sOh1QC/+bqi+kTr1jG2EE42AvKMKX/6LpD3WL2MAxAyotFoS+VHRmxa7UwXwtx9OB34j4s68E+nDvzrog78h0OrAH6/9e1ntTekfz4r8Gkc04JMdwu5z9SKRPzud92SoCFOgW8x2FPmpW9DHfiPizrwT6cO/OuiDvyHQ6sAftNIbr755tK3b9AeAHefuY9ZytLfeuU40xY4ClnZz+plli9V2X1QB/7jog7806kD/7qoA//h0LkHfn3v5q8b4OewBKx1uUXorkn7G+A3XKLSCmXmK2a1JIDl3fVKSktSB/7jog7806kD/7qoA//h0OoG962FOvAfF3Xgn04d+NdFHfgPhzrwL0Qd+I+LOvBPpw7866IO/IdDswE/gZZWd0iz21BilxWeTgJ+G98MV/TzndYqcu5zf4tUemkaA368UV7XHcYe2JFrynRCMxe8h1DqumiBjXOmNmaWw1w0BH7lNnCyxVP1IhdzUeSrJjKGB3PXcyoNgR+fzEQZtqvzcy4cpf74PNwJU9svuUDVHDQEfjKkzEOeOT9s99MQmcSzWmZ8syVfc9MY8JML9io2gX5t2oxoKnnfUE6mUAv4vQ/P6jZjm9TnEGhfwE8W8fG0bbQtzQb8lNjKeLaG9ddAOrtQTaUW8DMGNkkwiM/Ke7Z0xSh9+KbifeMb3yjrxweArCftPvcDqSiz7StNCTRYcGlqAT+lt5GDQYdf//rXy77o9tZ23xSAtBSx7XkNXMSXloNja9+xTRhOQ0PgRwTIXv+1oTLQ0uYWc2aFfGcYITIy9nYgC2dJQ+C3sqTNjIxLCQEb26oy/HMRPbHefaa1hiwLa6nR80xD4NeGdjTLgFxkLI8ZO0BnLrLvh5377KFRk33UzRJaksaAH7hYd8QGQw42zVimuQgw2xTpNEsFt4BfGW1LW+u5PUoOZSnmfQC/GWhmrWlzA9eNWVuaZgN+5wy8s786ULt48WJR7KnUAn7C+sILLxQhM5LfqnyUxwh/4MLBuH79elnCl6H9/Oc/Xyrl/meeeaaUyTK+5uzfdNNNZYeppakF/JgdsAeMDnsNWENAhoKnb19tSw8DCcTBsaECsLdPs+hW3SxEhFeMlcjS4V3eyWNUXzMdOAZ/+9vfmttK7kIt4Md/uwdq/5BVFDmB7rfmgmumTyqbutm7Wl0ZeXwxC0Md4zx4v01MtF0MNHD9y1/+Ut7hec96JyPDmHPs8Eh9AWEyTuSUXFhrfJgxmouGwM8J5fwCsjh1yv7EE0+UduOwKCdjGsdNncmCdpQVUlZlxj+ET+ppExAKq16cWntVcDSAJHnj7GXrV/z0XsteKyMeIWVyzn1LRzNjNAR+cmFtDrvXJYK0UNdTTz1V6scIa3P2QPu7h4OrzfHMOXqgrVMnPHOdPKi/6/hKh9zPzrhXW9ggzHvwlP4yyP73DKKL3oNnyrMLjQH/66+/XoBf2ekwXYiOaXOyk2/ikzqRfW1IxyxkZqBzshh4Rb+U1fOIPAqKkHeQrVpPNlEL+IGi2Vb1dOiXX365tCHeJhtLVtUH7/3Vjsqn7ORZnelEyIBu33OPdibH2oSdIPtzT78eozHgV25lI4u1baXL2ko5yVd4RefZMvI6JE4ozGLLPVtv5Y7f5HJumg34Q8BLJXb1+FrAT1Ayap/AWLf/rbfeKuCXpXeB0Ve+8pXyfYCBCPzTTz/9TuPYEQvw8lCXphbwExYKzqtznnA4CIfG5dAASwZAJiOZDgrLyXn22WfLjkruFRVQDJG2SIkDJMPh3T/60Y+K8Fjn4OrVq+Wc3629l6eSOgyBX/mV2zbECP+vXbtW2sY3X3rppRK1MRDaDUCqqzrIfnCERMeMvqWZGcdvfetbxdnDB9c5cTI95A5vLc70/PPPl+fwlAGjQFeuXClg++KLLxY5Ig+cEO9TDt90bm4aAj8CyOqZDaS++c1vvv2d73ynGENbT+MZL1/9yLXfX/jCF4qD6jll9ps84Gn4aq8K96o7p5AMAC4RHQeM4cUDRh44eBdn3NbWZAT/8DJ8d13Z901D4Ec3btwo628wmgiPbAcLFPzVfvbmYGOAo3U53O88+YnDSdbwhj2hN+qNB+QQrzwH2PGOM4bPdMwiX8CX3sgakhnfY48sE4732tFCYkB1Ko0BP93RpnSZbWBjEcfnlVdeKfrtL3BQxueee65EiWwgu6cdlYnN4ajYEvbVV18tdcIfeuh+z3q/unmne8hNDbpj1AJ+dv7y5ctlQzLOvY3QfM/qqL/61a9KZhIBme9///ulfbWdtlB2sqrsbLoyA3kRr/Pkn90ix9rSPcqLD9ohMrIktYA/sqgOZEO56C9nDL6QP3LDBpE/GWbyFX0mtzVpdwHqG2+8Ueyj4A4ws1NsN/6wIclcz0GzA7/GJYiJLKZSC/hDGp9yAzYeLuPPW0QMiAg30RVP2P8UiiIQKJEiRZduXZpawE+ICMqlS5eK0BACAg4glZ8yi/gYHkrBOMtkMDB4DSTVHfAzZBSEEhAk4KCLxWqEDAuQoIC/+MUvSlv4jmdPSy3gR0COQhN+zgoFEFUom1Q0YAQ8ykvBgbo2ko5+5JFHipBzatQX7yiOPcspAcUHUhSDwuERgPNNDg5eMWzaWj09I6tDoXxDeRlayz17bs4UaqgF/OoPyCk+flk1ktyKVnT5KL86P/jgg8UpZSj8T6YZAnwSIWlHBhdQKb+InlPLkDPeQI9zG9BzTV3ffPPNIjscJDIlc8IxEnkAEEaYrJA779k3tYBfRMSQ2umQDJMTRk+b0ge2Ad/seQ6IyYnokmNP/jg85J0+qB/5s2cHu6L+5NI9gEOUCUgAuawKpwAI4g3+c5o4lBwHPNcNoC2V7dFHH91Jn8aA37fvv//+IufKDjjpAaefzgBU+k3W2TA8IhvamJNDJ6TYATrHku1xkEn6xQ6QD/ymV2RCGejJ448//s4GZydRC/jZoHvuuaeUmzzSOfKsLLAgC6aRb+X3bWXXHnb59G1tDk/UlW6Ieuksp4c+sxt+K7NrbIzfZH5pagE/vSZbbC1ZVAflj2PIVisvXSOvnHV8xiu6rB2S0fIXr/CFfMFOdoyO4ieZwE/vVfe5aFbgZ3A0qsruSmPAT+gsyQtEEKFnSBPxM6SMACLEQJLhrAnwA4+zAn7M1uitrgYAyAgpIzDjGFAc+xLEsxUlqtOf/vSnomCUkFL4BsNAYPzPy2fwGAxGBrnGUNXAtAuNAb8UF0dG+Qgv4+hbIgz1cl77Aamf/OQnJUrnkGlXhg6pDwFnxBmwpLgIJ+PG8/UeQKB9/ZUCZDAoCn4BPARAGRrGwbdc43Ez4nMqUKgF/IgxoMCyTMohrQqAlE2bKBcQYRjJL3lmuPEsWTPGj9EAQvWy1OTYNU4gPgI0esmYyH4Yz8K447n2EqngG76KxJQL0InY6hUt90Ut4EfGpnCQZUe0OeIYkn/nyQAwEx2RE0BNLgG4KBNxgMgAW0CHQgF9DoDInz6yN9qNU4kv3kf/2BgOB4cCf8mW+32fsVfOqTQG/MpFb9lUmT73cUbIq/KrK8DHA/ZDXZF2Jtdkgq6JEAEj2SBvdO2xxx4r96Wu/nov2SSXbImgahO1gB/4kUnypcwOvMcjZYitZd98V0ZP2dURD8gjShDjPewm/rNXdJsu+C77gifq6jnjWJamFvCz7Ryu8Jf+4CV9046II8YWk0f6TY799gynLt0r3q/LIBkXjgSnBpaSPzzzTu/bJiuzLc0K/NI9BIqB2pVawM+oX7hwoUT4PCnAR0AIkIOiiqxEBJh45513lrQX8CEwSe0yDgwHg7s0tYDf9xlhXp96OAi8NBHFFu0CMuk+2QrXCArlUQ8pSYKmXkBCw1EMCslI4pv3PPzww0WAXHMPYhAYjqWAHwGvD3/4w8UQMJicGO3if9EVgytjQ2kYG4KtLYE6AnQUmtC7l6JT7vBAHdWBjHhGBEwGOZsAnlPn3Yih8w3PMdJ45Ny9995b+Dc3jQE/2eOg3nLLLe9kGrSTKEkb0Zm77767eP3SmumrxjP8RKI80YB6a1tOrndwsLSFv8CckTDQEd+BFIOBv+QAGGgT/AeIwIDTQfHJFSBktPdJY8CvL1u//u233174gYAEnpFvv40hEumLNAEeJ5JuARzE6OITR0p2AC9Fae5ngDmb2gyosBsMsKgb2OA1nolEOWGyDnjmPRwD7SgKw9+p+jQG/PSTnNfvY+iBqIOzytEAnBwU7YW0M+eH3QBQZN69+KOe9ELUCJTwhE1yH52hE3jE4aj7lceoBfzGVeFPba8BuHKRad1I6kqe2Sz8VV5ZGfrCTiMOhDYB7OyjtkyWkMxqY9cBoLrKjpHtpakF/Lo0bPlO1/CELLDLdJC9Uy92h44JyNQBP9xLZvAsJH2PXxwD2TlOPEyLLqrjUFbmoFMBP0NSk3QrxRw6BFMI6DFqGRSCKAoPkLfLuwXcGgJAAAPRE4WkKIReFOQe6Ut9xNnBj5dlcCBAXZriudYjh32fgBN89VA2Cq++jAnjxJtlcBh0Qk5wCJH6ATApO4BHGDk5DJJvMWoMhDoDUIAjleYeRHE4GqcliqBs6YOsyTngyziEGB6CLLJwkA+GFR8osFS8c0jUwQhL9zHISd0yFgwLOfBu3+Fhc64IsOeNAeEA5tvkAF/xCsBxCCifMsw90wFJpVP4pPBqougialEOAuyMt7oBKs6MdlYPoEevtKuIFGlHMqwLgDEhM+RHO8u0uG6siP/JETljLJTHeTxlaPEK37yHbHKCgQne+J9Tt0+SSm8BPwJI2i1lYg/IPlliZAGZ+pF7ckMutXfSv3SJ06CtZQfUFc84feSGDOtKUG/ZMTwjOxxNjjieaS+GmcNIvrQJkAS+nARynOzLtuQ7AGJozLWdY+hIsB8cHN8lLzIYZFu5kfJyHpWDc+08/dHdps54xUayecqPV+pF1vCSs6gNajAfowA/xzLkm7KwGQCJyLt2YL/YA7bYXzYo5aX7AkTlRJxAXXreQxfYRpkNdScL6q2tybu6eo7tWJrUla2t7biARZ3JE5tCTtkecuW8LBpM4lCrIxucbBVdh3E10XPAr42l/DOYd0miVwKPycCvkfR1zE0UjGBkpDOKMSWcjJe/dR/J0NFwToOpXH3vPgmPgBvjMiTKn7L5OxZpeQejTFgIP0/4pP5pSrNEm9SEr5y+1ujUMdJmU8qFL7IfDMUc9dH+lDW0hDwAfso/5d0M9JT7GVkyoCtkjoE+HAxtc1YEhIDdFOLATAFb2UIOJKMMbE5Lvh9nBE2VJboP/KYODCS/U7MLnqlBekjs5pT253CIQE96Z4tqp2Bb0lZL27JtSF11U7TsOFmoeQFEZVw4KK+99lpxqhLAavfaBg1p0/W5iQxzBmVVhjQK/ArIE+M187INeHH4DbTz/9TDs9JDvB9Al3cBUCDAw3b4ne/6LYof3utgVOp78w335P8lDt8QWfEIeYApm6MuXw7XlTHl4rU7J2qR+hHl6UciVLxP9zlqfvudqMHzKYfzBDfPpBy7HN6rTDxX9Wu9L9/M/55JuVIG9+TZ1FUZUx8Rg6yN6Fx7O+fePJ9n6nr6net5V74hovF99zlXl2+uQ1Yia0QMD99MHXL4P3VLuVOPXE85/XaIJKRLRaPqU19P/XMu9Q+vnKvvdd078Cbfzz37OvBMhip1ro/wY3gOz+oyuyd19r/fkXfXROe6Qsit+tb8HfIs7/R//e38dm34/dyzzeF7ykFO2M/6Gye9z335rv+Vz71Dvc55v/09iVf+4gf70+L/8PCscsseqEOeGfIq9+Y7vh/dS/lSRu9wvtb9nPeMI/fn2fob25T7NIf3q6vsA1xKnRzKUvPXX6l/XZAG4Orek9VLOV13f+o4PDZdn/NQD9kf5WtlP0eBX7Qn/SVlKKUs5T7H4V08dGkc6ZG8W+pDqldKWxTscM51ZXHeXynUvIuH75z78u763jnLPTy8W+pKHxuPSlla92061IfH6Lcoz+8ly73pUB6er/Sh+i1RFn3RDu3k/X637jsvB/lySDtzWJQ3Mjfn4b34jy+t62s6wh/Ro5RzeDi877THeeOZsuhLZ3wZ+l3twlkcyspJEHhI4atL675DOtRRXQG6buxNdQ62eAa/YNYScn3ag13VVc5eZQxNTSem+vUrzjmvMKRAlEIapSZM1Uetf8z3kTSb9Kd+u2EqRvrcwJ70q7rX/wY07KNvCEjz3nZd6OO8kpSUtk8Kq9N/SB/kEoMGD5k496KcYyOp/nqxq7WQoMw4m6ldDmsmdZWRODQ7Dh8FK7OM6p+DWqP6AbWRnVLMUt9Ge/OcpWGM9DeQwmhKTgDSSNIzRl56H5IelYaRftdPqtJLEo9PiqceDXoIpG9IFw9HrNP/09io/k7jNDaq/5BpbFT/Gqg1qv/QSV2Ho/oPgYztmG063xzUAn5G1WhH5NumiOhjNvUrAG4AnOkwFEvZTPcwqjIjKDkCHASAZUpPay79nNSB/7ioA/906sC/LurAfzi0CuCvR86a/mSOrbSque6ZqucZ8zyNBgW6pkj4PwtTSCl+4hOfKBkCy/gaobokdeA/LurAP5068K+LOvAfDq0C+JHxBOZLWvAhS5ZK/2exIM9YJCLTK0T6QJ5jAKxMrzCXmsOgy8CIcX035r6bz2pAxpzUgf+4qAP/dOrAvy7qwH84tArgN6ffoiUi/ETw5nha6UzhNY5pJsA92QELt3AMAL95pNL+GXyl7J41UNDzFjKo1w2YgzrwHxd14J9OHfjXRR34D4dWAfz+v/XWW8syk/rq9eW7j9HgDHAKLGtaT00wINAYAMDPGFv5zr1WUHLvjRs3/nvnMtSB/7ioA/906sC/LurAfzh07oFfBC8Nr0/eqnWWc7Tqlelkmc5noQdLk9aU5WAznU8XAMeAobFUqGlpS1IH/uOiDvzTqQP/uqgD/+HQavr410Yd+I+LOvBPpw7866IO/IdDHfgXog78x0Ud+KdTB/51UQf+w6FZgd/Ie2l4g+Z22ZgBnSfg15WgTpR1KrWA3/vsIW+pUvsLYDzDZzewKYBhIKJZC5aHtBpha59mDaurY+5Biy3gd86gS8Cn24VRS/tbTGnuGRPnkVrAb7lOumAqqQ0xtD2yOY7Bp3Pur71GagE/ebFCJ57Z9CiyQ54sEb5EsLFPGgN+9SQr7IK6sw1z6g3bozuU7d6VWsDPzmlHNiDENlkueVeyFwz9mLoZ0BK0T+DXLW2W2tQdH3eh2YAfUNt60Xaq9oo2CG+XZV3PE/Az5rYC3QU8W8BPkG3nee3atcKrbNU4NVI0zsGARg6DdrDL25BMd7T9L2Wdk1rAbxVFaysYPGkhJftPG4BpTWjrXJs6eejUAn51f/bZZ4su2HLT6pKWPeWs2W/8GByik6gF/GTlmWeeKRt1WXDLFF3LIQMtW3O31hZfE40Bvw1Trly5Uupt4yJb2c5dV7vFWW9/V2oBvxlR9957b5kaHVKHbLe7CxnPpa2Hy7afBe0L+I05s+2ynSTr7e5hT8u+n5ZmA34L5lgYx/r0jNtDDz1UgGkqtYAfyNjYwo59drXKeymPzYKs4md6n4jAblzAOvsn2xfaLmB2yUvjGelvo5mf//zn5T5gxmgTXiv6AVYgbd7/XXfdVa7xmKdQC/hFeMAYEKifiB0vRTOiQd+hMIxA9mIH4GYkKK/38YRFQhYn4h0ynOpuwKK628ENH3jcgBgAW/VQu8yxt0IL+Bll6yvECVF20ycZCnXh7CiPNco9b+ClOhBqQGmaJYOvjLx9/FcHhD8EdIl9IeakFvBz7uyoSBZE/4wZo+heCg747QWfKapkBY+QqElbWrDKb9kbESHgIJsya9p8zdQCfg4tgNLuNqbiRJqqS2fJM1DgCOAH8jfObfSavti7g51gI+guQ0eusu7HWdEY8AsA1J2zzDY4lJmukH96hFf0gK1gI+0YR+c4kv6qu41k1JddY/M44rJwokk7XtqYxXXbSLMLbCrZ3IZawM+ReN/73ld2EFUOJPgjt8ov0/W9732v7AbHNpBdcszWZofCOluAyDaH2V73bLcNcpB2r5dlx0O6oX42RIMF1njxbvbEFu/qypbgld0F6ag2IHvKTsfgxViGogX83um7bDX+ahd1ReQThrCR2o09Y58tGa8N6XcryMOHixcvlnrXDg9su3HjxuyD0WcDfl4KUHvhhRfK/PmvfvWrO3kqLeDX8HfeeWcRhB//+Mdlm01KDfyeeuqpssMXYWFMRVn2sHcPAbC8r2cYDw1McNxHMAmHKX+effzxx8tCPpTBlooa0LsA2El74I9RC/gJEQW3ZwCh8X5lIsQ8Z84S8ObxKxdAVSbgbWOcS5cuFUFlwACKLgPGQp3wQgaB8lM0iq/uFjEihKIJztNpqQX8vmWqpS1jKaBoVnTLcOO9MimrpZLJhLa1jgLAo1TKqM1feeWVopzqrL0I529+85siS+ch7XcStYCf0aLI6s8YcIylsbXXl7/85WLkRXaACknxcg7wRaTLUJAHbUteyC2echj8jkFcK7WAHz+ee+65IudkQPTPeefwkh+AJTICJIj8kBfOJjnynIW+ZKAAAT7JPDL+fre2Id0njQG/IIPzDKyBFDlh7NVHXQAHJ4jNcg9b4D7O0JNPPlnOkRMyR7/YQO9kG9kwwMKhApL4yUZ7J3vEIdgG/FvAz6Yol+wMmWb32C+2jc2KbnMGtC3ZVU+6zvY9//zzpWzq6r3S3ALH++67r7yHPWDHgLi6s20If7yTfXjggQeKntjDhQ31Xr+Vib4ItOgSPaSD7L3/8QXfUv4WILeAH5iTQfKJv7bh1aYyNOwa++h92oizwi7jPSfLdUBeE9vBnnvu+vXrxe5xRACz+9kCfB5uRHcamg34VZqxJ6iMP+BS6anUAn4NpfF5v4AHsBAsCgFcKZPGJiy+TfEZXOd0OxAy0T3j4JzUlL9AiSAwvMBe9ACYCI0NgAh5IraptAn4CQyhY5wAP+GnxBwo3iTHhTAzdniCOADOAVGbFYmUKS2jSEk8z3GhoISEwCgDgfZdHnZLuKfQGPAzJECLUPOC/a+/krPGyVJWdSEzFIdB4Fwpo98MOb67h5BrW8BGKYfgcB6pBfyMi24doE3eGGYOAK9f+wJ+m0a5hkRHjBkvn/dPLsktwy5S0+aMB4cWX867M7SJxoBf8KCebIk1N+ipaJKsA35AH+PJVjCyQBDP2AUyx6jbJ53u02U8pctLT+PdRCcBP+ecfgNvgQDQYvfYUvZW+9NxoM4Z9q4AHruhnuydiJGNYAvxBQCRJeDDzgAtdg8wy0Bdvny5CQBDagE/50G5YAL5Vg/twdb4P+0EHzhedAROCKZkQMm/jCCdAKR0n90QSLAxMhhsgWCHbcseK3iobbWr93IYPEtePOd+fNMNKbtInvD1wQcfLPf5Jj7SIXrnHfBlSC3gB5reGb2W7cBrcqc+yM6Tvs9WkkUy6x5Arh1kDRDbf/Xq1WIr2GpOLxySzeP0eFb74GsyKnPQbMAPwABaUrJ2wKOEqeC21AJ+W1hSAIKicRhNTGf8CAAi2Nbil0ZSIUzToBSAAeAsKBOA5KEBIgeQcp2g8XqlxGQuKBEhZWxqkNuWWsCP2ZYIJuAIb8Ifhp0yA1agTZkBJmDksBBsSs1r5e2qD+HDCylOSqkeBI+g4APFcC9ynse8BPDLTBBoESvSTpRbXX2TccJrSgn03S+SA/yUA9gRQErM2Gt7yuF+189DX98magE/ZaXwjIt2ZtBklhgS7UZGXKf0iCPI8cEHkQvjr/10GVB6YAEc8MW71k4t4Ofc4huAxksRHZAi3/QdgLiH/iNOAb1ggB955JHCL3ohypLW1yaMKcPOFpw1jQE//WSbasdEVyDHkR5xgukd/aBTHHnE7pEhAEX/gS7Q4XgDNrYQ/zznPWwr4LeJGfDlRLovwcVJ1AJ+DrtIn3zLVLE9t99+ewFZGUCyjETxbDibTPa1PV74NhDWNn6zu2QC2OsCZQvoBCCnJ8qO4A/HAk/YOeS5OEEww3OCRnUFygA/TjS7z74i7cGhypovNbWAX7YZXpDTyBfdJYuCH21ITukp+ZWV9i2ZFuXgLMVOuE9dyTAb+sEPfrDY+Dj1nB2yPfeAv9mAHzNUADMwQKV38VBOSvUTDBEBYBYVa1hKjggdkBAh8Qpfe+21ItTuwXTGk1BQOGAez1pDaXhM5x2my0JjS8nKHPBapzJ+LOJntCiHb/OKOQFAkfet8YGmZ6R8ZEx47DxqistbVReg7l7PBTSV2bvwHugSfArhHsSw+OYSwM9Rkb7T5soniqAY+ErQKax2ZYyUQ+rNlsnSr4wVuaEM6iSVR+i102233VYM2xpoLOIXDWgXWSjGxTkGgNGiR2QL7yg4npFNfNF27iWnDKWsDoMiOmBI9BuuncYifs4ROXDIBpEp+k0npOpFb5x4PAOMnET2B9DgF77Re3IpUPA+WYRWRLdvGgN+OgPUauAHpow+mWBvATc74Bw7gugHJ4DdYG+cFyhIobMvgJnd8JutY+yBsIBC1tF32UrZp000FvHje3irDBcuXCjRLd3Wfpw0QY0yaBN2Wjt6D2dHe9VEP+gKfKEjAXBlJ/9siut0SP0C/LIZviNQ8A584wiy4YJQZZIVUm86Sa7oK6cDP7YFfriAv3BG/Tmc3kVv2bA4o5x3GSq6r43wQV3wKKS9tZ1vKwcnpe52ofcCpQSIc9HOwA+ohlP2gDFQ4OXtOojGwBXGoB4fwKgCS9/U0AAaYWqdhpci0agMg0ZAwFM2gpeXARxAyD0Oz2ACBvirIRgk36CkgJOS1gq5DfHWCeJw5DYDxavlCaoLflFE6XLGDVEidVZekZ06eRfP0nP4z6kCvgaHcUrUl2B5n3YgsAQ0AC0rsI1Xv4nwRDvUjhlnQPSgPspHUUQdyDfTFspM+Xj9FJPAI/ylmBQC35C2ZaxrhTvPRM7iZIWUXZQRuWXwtQuDpn74hgcie23MoYuzTG48p03JI3IvI8fIHAKREX3ONZEV8kOOGFWyQb7ZGtER3skqGphF1vGUbCNyH72O/AEBAIGX54XUifzXpLypR02cYHLBhqmzwIRtysBO9pJNAQwGAjtPR4E/XWM/fA+AsBWxl4DXdXwGxtsQO6Us3h9SFu+tgUnQ4Z3Oqaeyk/E4B2xtgsaWXWJb6Aebq/7ez+bRHYEefImu+L66IoDpt/J5h+fojHfRI2XACzzzXX8RG0m2WuDqXQJIKfmavJvOahMOkffRd9+R+ZTFYL98Q71hGj4ot3q0KHZhbpBvEZ0SqEwCfsZZBVXUwBn/O0QhGA1Ak6bJtW0OaWAGlHfLywH+Rn4TXh6+ezCN8PvNAHgmz/umbyuDsvg+TyplynMpJ8Prt294l7p4xns0gv895x1T6uJ9FI1hIojDMqpDfXi/74Vnnlc+QqNfUiTN2xfJayj18Iz3+qucNe9Tp9yT76b+ux7KRrlEHOrnG877PuGmpOqDr6mLbyqP+5THNfcqq+c9i/f+d7gmUhCl6a6h8FN4v+9D2RwiC45LXV51r9tZ/ci0I/KWttZu6o5HeTbn/aUPUt3GojD0nq3LsaYjPBPFcYjyv2tkpeYZnuCRA8/CP3wJb8hanvV/ZAyYii7ZDk5BvnFWh+8DXmDBpg3tQupRH5GPHJ6JrXCdnETXODl+kw1y5P7oFt445x73+h3+bWMXfJfDTjfZgPBSWbwr//u2d8Z++n6+o93cl3Z0v3INv+9b7nFv6uJceJH2dT7f95y6pSzu99sz/nrOM6lv/d36e3U5/G+MAaxjx3Pd37zL3/yWDZG9M+Ba15KsAsfE+13PoUz1d3LELgzLMfeBp4JK9orDM6RR4FdA6RneIk9Gaj4HwXDU57Y9eP/6SqR/eOj+F+lyBHxP9DT83vBofd87hue2Lec29wwP5Qb60miExv+t+046fJeBEAlL9UsdSft711iZtq3Trod3awOpevXb1BatY1PZ1U8Kl/IABTxYsk6nPZSNfEnvSem1ZG3TkbqPncdnuqb/VneNiGLqN87Toezhmb549avr4zc5UE9/3Vs/73CupQv+9z7X2A1pbH2mYzxzrnV+iUOZGVspYjKuTK37WkeLB5uOTXWbUneOCvnmjKvDlPJM+c6mY9d3Ke/U59Je7Lhova6zd9Xv9JttNHZAd4NnZHZrGa2fP8uDfsiA6zJpzXI5MeKXTuPN8LLmPKRvMEyqKOd4Z7wq3l1973k+ROtSlqKz1vVtDvVVbx5uvOaz5gFvVYrQ9JrW9TkOUYs689Rb18/jIXrlpPDuW9dPe2h37U8O1qQHJx26s+h6fU4UJuoRGev+El2SBzagvk/w4R5/6/O5JqrBp+jN8J4c3rtPftIfkbOUdev6eT3wSB+0fmH2v3XPIR7qKgiVPWpdrw+yRN4c591+yUyyV3RwSBv7+FVsbgKYPBIGdM0kXcPDG/bxr53Sx8+wdvp/0kWVgaadtqNWHz8yNkWa1KA9A950owwH13IGOA0tOcz4CEZ7ExlTUA9U3QeZujbs418D6eM3pmLqeKc1k7q2+vjXTjv18QP+4aj+uchAD+kIwLlmEo1IE61lcNq2xJOV5t+3sTzv1BrV3+lkao3qNyIoB+sAACCPSURBVFDK3GXAbzaP7i3T0UznEkkZwGa8A0MstRpwJ4/SliJqA6TMIGFLjBOI861tAL0snPEoMgtGnRuzIrO2D+I4t0b1r4Fao/oPndR1OKr/EIguzTKdby7qwH++qQN/mzrwT6ch8IuurE9hPAtgRkY4mzFitLoMmsGNrgMg/eSmoZkNY0yIsTD2RDCl19Q1A5f0Y+qaYqvMEjDnXRbByHBRt7U/TBM0iGsf1IF/XdSBv6IO/JupA/9xUQf+6TQEflG3CNxAqhZZA0L0L6L3rKjeb2tYmPOvm9CgJYPnrMthCjCHwAhmi0VlyWuDRq3yKZtgXrVugX2BWQf+dVEH/oo68G+mDvzHRR34p9MQ+IGiKYtm9WQucyJ+0TlAdyBgbtEVQGTqVOZyI3OlH3300bfvuOOOkgFAugUs1Q34ORayA+Z1yzAA4n1RB/51UQf+ijrwb6YO/MdFHfinU6uP30hjKxOyAQbuGQEvcqdLpjJaGRRlfwh8l9Y33ZfOSfOL4q15YRS6efxS/RaVkS0A9gZimuZn3IB7TZk10ln/vxkBS1IH/nVRB/6KOvBvpg78x0Ud+KdTC/iNNqb/1n93AG7yxlBJ0bM7SN++zIDBfmYHWOjK/eZPmzcdR0A/vzEBpp/SRxG+1Q/Nt2ZjrI9g3rUZGZwB2YIlqQP/uqgDf0Ud+DdTB/7jog7806kF/CFzooF6Pa2XUwA4kS6ADABERuhzBszZR66lu8DYgUxB8z66mWed96xvifxby+bOSR3410Ud+CvqwL+ZOvAfF3Xgn04nAf++iVOhayHOwlLUgX9d1IG/og78m6kD/3HRJuBfGlDWSJuA/xB51oF/XdSBv6IO/JupA/9x0RjwW3LW1qlGl+uTtgrYEiteGvnOMNfp7zGyMp6118+axoDfwD3b69rK1Da95u+LyC3OY6W/bXaTI5947ZnzRB3410Ud+CvqwL+ZOvAfF7WA34px9tL/2te+VgacMSB2WnzzzTff6XNmWOpV4+iUPueaXK91TSTsO86lz9tgNSPetQ+ypK0j5Bnf0gdu6pvR8omoOQtL6PImagG/bbLttW66nRH+BvR99rOfLf3vAN8UPqPxER7qs+cUIPVx4IGV/fA8wI9ffmcMwFlRB/51UQf+ijrwb6YO/MdFLeC3stxnPvOZsplVyJQx9wEhU89sGfvGG2+UeelGmFtwxkEpvctfo80ddI5eiIZlEIxOFxUbmGanO3PcZRNEyN5hK2fz12UdjHI3390Oa75vnXuOiSyE1ew4KD/84Q/LtLZ9UQv47dYH+OlPyH3A3ta6Ru4DoH/961+l3Ebk27WQw8B2cBi+9KUvlSV/rdrHMcBz91roxzoBpvmdFXXgXxd14K+oA/9m6sB/XDQEfpGnOeJAHdl1jjzY1ZLht8ocpwBwkXmgf+XKlfIOC8y8+OKLZTtZK9DpKvjDH/7w9rPPPlu2rf70pz/99re//e2y5rwMgne6x/3mq7vPt7zTe37605++fe3atQKqANJytYDQVLcHH3yw3GcDlmeeeWavgDQEfpG7+fcAGxlsx+iSN/Wycyd+cQJsDa0bwLr9HCxT9tgN9XnrrbcKQHF0dBv4zSnSRhwHTk6dDdkndeBfF3Xgr6gD/2bqwH9c1Ir4Rdwvv/xycQKkmKWt7TgHmBl+YJW+dkBm3XgABaBfffXVEvlagc7a8sBNJPuDH/ygOBT69JH0vswBcPeX03D//feXJWx1Kbz00kulGwDg6dtHdBfwU3rz5GUklNv7LXCzL2pF/Oomu4HYF7ImUufAAEz8Yx/wJkYL+FvqF4/M02efOFbuAfzqjacyGi+88ELhSQf+6dSB/3CoA/9C1IH/uKgF/CJTwA2URdoWkfn1r39dNpqxfz+nIMAnYr969Wp5j+dEvQ73ukd0D8zphn7ubOsKwKX5OQSA7Xe/+12J3EXI1qcHhtaiF0lbsQ7JGgBTzoOMgr5z7eq35/dFLeA3eI8DZLU9tsBhmV39/PgiVY/PnCZ147SQR04L3gJ1O/ZJ73vGBj6uOY+HVvmTCfBe3SsZH7Av6sC/LurAX1EH/s3Ugf+4qAX8yAA1UbX+eGAlotUHD7BE4hwAZPEZkT0gtwqdVD7ZMagNSOvTl0HwHZF81qa3pax+fDvPySgYO2BpW/3eImfvZLC97x//+Ed5RpbBOAE73rluDIABfvr7k0nYB7WAHzDeuHGjOCoGRYrg8Qxv8UOGw3OieNdkSvBHHW3jq2tFv77xCzb1MciPQ6CLQFcIZ4uDRDfVF9/3SR3410Ud+CvqwL+ZOvAfF40BPyLLFMw9dAcZkY6H9dQ+o/ntNgfUKCYCFCJV0Xv0zXOZCaAf3HmD8rLOvCiWY+BdmSFAHvNO4w0853/vyip3w/IsTS3gDykvIDcOQVmRejmfuhvU6B4D/ZC6qgPeMtju9dtzxgfgievqD8BkDPLufVEH/nVRB/6KOvBvpg78x0UnAX+nNp0E/EuTzAgbs+/26sC/LurAX9F5A37RyrY05d7T0LEC/xyGVBsxkDXtq912pZOA37lh+f1fHyfRtvetjc4S+M+KNgG/No68nLf27sB/OLQK4KcA5vDqx9QIRuoiIGROrulIUnm1okh92mkrC3gYVW1wkKlMjPTSg3qOCfilh//4xz+WAVn4q430YWsPaeq//vWv/71zMxmhbXqagW34R8Zs2MJQalP/S9WeN2oB/z//+c9SF3zBM+n6rKznmgF4BukBwDFSdzKOh/7iPx4YG7DLFrJ4+Oc///l/Fgmyw520+tDhWpJawK+eZCngIq2PT9H5tdMY8LNfbKrxCA5yY4DiXEQXyd9p+NiB/3BoFcBvxyzTcAzsMfjp+vXrZYQ05TBlx1QdI3UzMMkoZQOk7rnnngIkiPE1/9l0KX99Y0k6JuBnTEy3MtrcoDGD0IwQ10YMXG3ctSvQ0fc6jI4BkrnsBrIZ1GZQnGltQOnGjRvFEBqkRhE5cvXzDOpZGqQW8BtE9uSTTxaZs6CMufT0xj3mzRv451Bvjije1qQ+//73v0vdOa0cX0DIacDfyLapafku3eQs1MT5zS53WT8AL52Ps8xJttKdtsFLzhXjsCS1gB+f3vOe95TR/cgIfXJgACMiN3SrrqNzHCqyNXTonctqfbV8qLf/hzK4NI0BvzUJLl++XGZuZIbG3//+93JNnehbyq/M6qyNMt7B72G7kwu8ijNnRgTdCeHtlGmNHfgPh1YB/AY78YIRQWa4ADmDQImQkcxGMRNyZeMQmC/tfchoX6N/GU0KkEVClqJjAn48BkTWRmdkAbWpaYRKlBFjYTS7edhWj2PcMigLASFtZj/1gBHQ40h4D4AgqKZ6GR3vt9XnvJcQG9GeeepnQS3gB2Kvv/76OwPv/OakGmn+29/+tpxHDAsjT25Ffoizi8/APgBgOp6ldo3GNy8fiPtflsXodHpjpoBR/6JkvJEl8L/zyshJe+6558qKfw56BTw4IiJt3+eoWPkvurUUtYCfbNxxxx3FYVI/DovpeKYZ4ht5AJIOg/U4KskQCQpcN2BPnUxjNOpf3fGDjMhqIHyzZsHSmb8hjQE/uTfDQn3IczJDdMD0Sw7vL3/5yzIDgzzQH/Klbtpd/bWnmRva3fu1u/da34F80Bs2CW/oouet6Bj52kQd+A+HVgH8Icrw3e9+t0zvESWZ8pMpSqIVjgCh5sUCeP+bx4sYjkuXLpW5vYwKQ8dIS1MnAp2Tjgn4TZ361Kc+VaL+RLYyMIyNOeYyNQzv5z73udJO5rV/5CMfeZfQATPPi2yHJHNghTpt+MUvfrE4eQx4HDsywNEwV/6sqAX8jLGV5GQwTD277777ikEG4ORQZgrwWHzm97//feEd443HIj/gK3MVp5ezy7nlFHCSOBJ0QXeBFPnHP/7xAggW8REh4yWQBxx4bn6/8+QfKGhHPPS8dwNT3WZ33XVXKbs2WZJawE9W8AFgm3qnTfEOb3VvcOCBtqWOTZHEw6effro49bIEMkYcUMZaZtA5jgw59E7OAYeL7eBU7ZvGgB+wf/KTnyxATV44KeyYKYcyaYDaWgTqqW0s0kSX1PGRRx4ptsb0R84Dx4cDZKonPly8eLHYa043fuA5ufBO37JWBB1iX9nDZAiG1IH/cGg1wC8ipAQiS2lKnjDjyQgigg/oI7RZzct1lZQylj5j5ERdPGXKxaiY88yAzEnHBPzqyBAzOpaWVW/Gyn2MD14zVuZVI2DHMeB8hbSp7hwGvibywEgCOOBGBrLIjHXZvd+7GcyzpBbwA2H1pGDmzuMP4+s+RlzGg8Mpegdq6up+wCYrhT/qi4+IzgEv9z3xxBNvv/e97y3gj+gHPuMTPoveRXOcAaDPWdJGftMhTpl20C0jc0IXAC7gB7hzO8ItagG/+io3G0OfRal4hWd0mcMC1GQBHHjhPlMXEd0G8pwC9QoBtMiRbiTtMLfOb0NjwJ/uSraP/pimyKEGynSJM6etrUegnTiFiMPmN7kjZxxFWRJOAWcCLzgG9ISdc55zqVuBQ8jZwz8Ol/db4Ei7tKgD/+HQKoAfiFJk0V7drwnY3asSjCqBDvFgRZiMBaVnKAMqIijGjyBTQkf6QOeiY0v14zVBQiIH/NUeFk2RpZF2FZ0AKFkY7VELnTYUzTDy7kHaR7eOVC4wYgxFvN6V60899VQxYlMGEC5BLeBnTPEg50Tb6g3s8EdWCvg7RwbdJwJ7+OGHC8DjI+AD2sg59TdgEkgAMKBAB4CECFZmRbZABgSYiIA5u84rjy4Gz1q1Tr8woFAubeo6vWb8h/3FS1AL+Dlwyo3osHE6d999d4lOXQN85IbDIushI8ABCPC7xyqIgJ3jhYe6BDg06gvovA/PgHAyhOleWprGgF/b4n9NbBK9IhN4pQ6cXI5RHD5pfFkM+sPeqDdd0P7snHYnX/SQfqk3fRQ06daRAeAgyDjJonLc2a4WdeA/HFoF8Evd3XLLLUVYGT7GisLrv2T4pLgoP4MY4iDYjIPxYFCVV7qYdyzNZ1DVknRMwG8EP55KG2sbbYH3AF5UAbi0p2gOkDHe0slZfS6k7UW62tR7dB8wbFLOwAhQWnRFZiB8TbfCWHpyX9QCfn2yxiSIMjmuIm6RNf0BXMrNGIvIODVIev5DH/rQO04UoFZH9/nLSJNpmSptADjxXpZABgSf8BfAyaLEWfC8TIAsAicK8AN312VQRMcAxV9AM2XQ167UAv4MbkvGAb8eeOCBAkiiVwNzyRMnUiCQZXjj+MkI0HXjTMggectGRkjmRfdLolpOjwh6X2A2BvyyNQC5HnPAGdE2nGEODTnSPhzfOEfAncNgbI17dYVod3VWd1lOzrEsiYgfH9lGMqLt6RuecYA2UQf+w6HVRPyMnejdQBRKy3tHhJhXaxWvmgCUcyqIKByFEP1oxNpAL0HHBPyiKuAvgtA+jG4GroneHdoPiDM80pbPP/98aeshkSl94KIPbaudtB2g8h1RkO+QD4O/gOcQPM6CWsBvQB+Z5eAos6g8gIYnQBhfRP0ML54BOkY7GShA4B3ej8fqTabJtmu+hw/45i/ni+Mky4W807McC9/0THipLOTUs+4zyttf3QBnlepnW5QjEbi2l5JXH+BmAKfxHbr68MD4HAPeAlzkMhkj1830wT/1Vk8yyKmMfWH8jAEg1/ugMeBXb8cw8+B/AzLZLfVQTm0U/SNjniMH6qft/NaugJp84YN7/I3dVH+8oWs5t4k68B8OrQL410jHBPzbkHaVhhSFiFZlcYDPrsTwi5p18QTkzpJawD+VgKD+VpmsXUhGRBSnP3cN1AL+JUlaW9aFUxniVHCEzjrVvwbqwH841IF/IerA/79E2ESlIrrTEoUUwZwXIzQH8IveduFrSPSbaHcNtG/gT3R8ltSBf13Ugb+iMeB3Xj+TfrRdnYLzBPwiUoZ0l8i0BfyiCt0NxhcQJocBO9KXU1Kr+CztJ+rVcAz+kKRFGda5Dd1JwC/lqu30OxqMdRoQWxu1gF/bM5QAJ6StjOA3RXFp0l1ABpZw0OegFvBLa7Mh+t71Z/stIj8UGgN+3WPO1X3tbI+FlehVi+iX8S/b9M/PQS3gZ4N079Xn1OVQgHIfwM9msOOyoEtPoQ3NBvz6H6Vx9Z8ZZWrAyC47X50n4CfoBhLtEkW1gJ8DYYCVObQG8hjQY8Cbfu8pZKqWdLmBTUZy18AS0m9sAI++wTlpDPg5LwaZGWiFZ/7qa9aex0At4De47wMf+MC7ppUB/VtvvbUMtlqatIkBbvsyJlOpBfwGo2UFO4P5TMM1uO00S82eJxoDfqP0L1y48K5uCIP4br755tJF0SIgTMeNHdkHtYBf+5mOWttIA6h/9rOf/fe/ddM+gJ990MWnm47NJB8hTp9xG3PTbMAvcjUKW0GdNw1L5D+VWsDvnaJIU1KMVs7UHStvGZjDiIpujOw1QtfqXyJiZLSzUa/AKOc8D3Q965woTOOaE6vPmKLxooGyVcREHVNTuC3gx2yjq43IBaD+F5lzCGQClIGxc52SIalx5eQoKJ9BYhwFo5qV3TcM9jF4S90ZTHyQBjfdiTNmJPhcXTMt4OfgKY+6JcPgunZQLhGb0f34CfgMJiIv6mkwG15TMIJoVLLpWJ7nTLqeds+8f4OStJ+6Etx99c+eRC3gV+6bbrqpjJzOlDGjs9///veXa9qdrKubtssKavqdyat7OXmIHHiGQw0cyA0DgQ/4ZS8Lc7Tx2DnyRA4MFEzUj+fkW7vIODDWwAXPPUtfPbcvagG/8gFB/MJLMm5Uv2jI/7JlysupImtW8yMj+GXGB/mykqH3GPNg/AfDSpaQb7Ib5PgsaAz4jVcxm0PQhOi02UqWLyZXdIYOqxfnSH3IhpkNeKD7zPROvMGjGpznohbwy8wYN1FnFjlr2oLuZxl17WjGioGunjGDxD2uG5PCFsRBZTvNwlBX93FsnCMDZN150w/xcmkaA35ypPzAmj0kZ+QTf5TPehmCMrZR26ijtoEtwwyyIC7rSpgRVU/r1Kbafe7BtrMBv8YE9gwKo23uqEaaSi3gZ/DvvPPOAh4cAB4moybCNMXJ/ZTJnNcbN26UBsFARo0zwkiY/+pZ73LfW2+9VcpnLQAKZEoVo+oe08VEyu4xSppRnwouLeAnROZ1W0ENkANkjWz0ukZ46KGHyjcZZqBNEXjPERjzuzkGFMjULeUGtkY5ux94KD9lESFpD9ESQ2AhkDlWKmsBv8hSlJbsQtJ/2oi3SklN35PtUCf1UX5GX8SivhnNzlEz7QiftKWpV2QNmGlTxke7p63UeR9TzzZRC/i1lTJrB1Mb8YxHz1iTSYaRwQe6ptyRa8DLEJBh9fPbYD/GlAOY6ABvTO8iS/iLf0CDLngnvjKq7jH1FdgxLr5laiG54gADVe9mAMgK47ovagG/OtI5zjyd5bhyKjk26kjOOSicAzLiHBkBFGSKDnFw8MA9ZNF5ThPjyVBzGPcxa6FFY8BvCqXsDB1g+7QHh9F8fDpB/5VdFxqZ4uDpBpBZYz/UlX5pfzNdBAlzUwv4yfy9995bnBTtQY61Aflmh+gpAp6um1VhhULXtcujjz5aZNpvB/tiCit+kNVMZbT0N/tnqifHTtY0q7IuSS3gl2HlrNNRuGclTDpI36zPwd66TrfgjXprL/dqX+WviTzjg+nq7Ch7yn6TafjGfpgJNefKpLMBP2ZQKEaNAbI8bubOTqEW8PPigXWmLzGGBIihpAhA2f8f+9jHSoqJsDEeHAUGjuEgMAwdT8wCHowKwLHcJwfAcrGcFnObGULCSMgB6y6D0VrAT6gpKOMvi6DshBsgqLPzQMwzlJ5gMdxJc+IrMAEElETkn2VNASOh40xwDLxDW/gGokjqWwPTLjQG/Bw9hggpF77hKZlgtDhY5AVf3EuhecF4TbBdp8zAjtEWRYhs1IGRFjEAPNFuVsRTV0bxrKK3mlrAn0yNCICMMWgiG22lbWWyyCiZjZFnHLRjnBmRnufuv//+YtgBIl6Ra8Y2RoTDwDiQb3qBp9qDfOAd4MRvzwPPpBTplSlfyL0cin3RGPCb6hn9/uhHP1qyXDIcyuxaVqszpx+4kAsyQtYZVjxTN7pDd8kjOWJH3Nsydvuik1L9ZALIsZvalfzTBbZB+wJL7ave2l69OHP+0ilOElmiOwB4bmoBPzsGrDiM7I6ykCnloKfkH6kDG8TuKrtIGFgKzLQdG8FOwxZjBOgLZ827yKX3ajs22ved972lqQX8QJMjrS3In6CRTPqt/RCbTH615WOPPVbaw/3AXZslkJQpSBY47Sl7hQf0lU0UtOFb9HQOmg34RXZeJNIT1SnsLtOSWsBP4EThUpMUGWgAHx4yJUJSIhjM6xIJUxqggJn+Yq4yiTYJKsVzuI+HRgClxxlc4xQYW4acMNabyWxLLeBnvJSD96d7QaP7HuESjYvEgJhnCLuGZ7DUX+rPOcpA+UR06qV8PF91UV4ZBHzAA2DiXsRpWAr4KbEoxPekHaXupffVleGi1NpMiku7csqAtoyAeqsTI02xyQ6+qbs29U4kbYsXjIV1G8iad/CqKclZUwv4tQUnVGRP9hwiF+AG/IEwudOO2gbwqzuHicyREfLN2bMIi+iWouKLNuAAcxQQg8MZwhfvBg7aHt+NBRGBAHz8cy+AJCN4njS4e31jX9QCfmUDgnSCbqgj2VBGMq+MnsMn4AIM6Q0C/O5l1PCNbrAZ7BQ5kh3Bh32kiMdoDPhF7tqVsyZo4hBzqNki4AEQ1EH7ARG2QFsDe3YWQAAW8/Lxhf2auwusBfwAn9zV44yUGVCRc9k79wMzbem8CJhzRv44sYiNYM/hCLuhndnuBG50RP3ZFTbINxNoLEkt4OeIWhGTnnE+8F7b4TunhrzJfnLAONLsnesJPAWVsRMyUjIGZEJ7sYf0XwDqHraRU6Ecc7bnbMAvoqB0FEtkAmh2GRTXAn6F09cucvJegqPfJ2lQxMMiOIBcGQAOo+IcowpsCaF3UTKRqDQxBaOEQIVQYbj3MywMDGMsdSjbMIVawA/4GDYGiNAALnXSABpc+TSICJ+RI2BS/6J70YCoj+AoL4VQd0pC2d3j3erPKKgnBQx/ODyEaAngR3hFiaXh1U1d/CbQFFhbZb49JVA/UR2jzbAxKDxk19RFmwE1v5GIR30YCIZP2pMxAVxnseb6kFrAL+LBCw4f0BXJc+CU26H+eKbtySRjAqS1lTqSDTolXciJIJcMp/aVKRJBATvkOhmml3jqW8pDPkT8ZD3AzpBqGyCiHThrCH/jSOyDWsCPX3Q1WRz6E8NKlsmIaJBc4C8jSteR6BMfOZ50xO/YEfU2UG4fYHESjQE/YKdXskDSvv53r/YDHmwqW6X7kswAGLwjJ3hELrQf8CEHbM/c1AJ+QRN5qgdyK7vMnaiYs8vuADPyK8PFPsMOoEkmEVnVpuyK+8ilunJQ6QAbiRccYvrkOQHA0tQCfnIro0angHK6LozD0R7kUlvpguK8sdlkFB/IL90NaeNkdrWhv2x+iG2Lfs5JOwM/MEw6MoQ5FIwxqp2CKSSV4/naaRAxZWcxh+8jwFin4UUFlERDJYph9AgQZyLPER73uNd9QF0DYQbDLaXCCeBhUVACNjVKUC4NPAQlDgplYbB4y8rAE9S4vuubBFvdlM07CAIlZwDdz5ipl2+IkKXKPO+aQ/04Ga6FP747x8hofMDP2jEL8foZX2lK5c5oY3xXloCi8vKIGTqG2P+IQnECvIMD5n2eQ5wG10XB6pe67mtE8yYyzgKQ1kSW9b8i/OfU4oG/yq0ujCPgl/3wDjJIr7S382kz+oQvgB0ftAMZEDEgDoXrIj1OCPnBJ44EecJL7yR3vuW6DBf++iZyL+DZF8lUceRqwhtOXh3dyPbEQCk3udEvSiYATvp6Odt+ezbn6QFHgMzKSg1t1r5J2XRXDWfbaEv6rl2VO3ZD+9Af9pBOsWPkRJaLzvsLnNQTKOMNR7Hm31wkW8VpqW2hCF2Wtw6MlCl2VfcKmVNfwKhe2pfOayPtiQC+ejsf28l5EdQ4T5a9l0Oobr65S2A5ldSVnR7amcyoEtQYsKhOdJ2DSjY577J5MFGd3KdtWkCLT/hKtzlXS7TdkMiLLMok4NdIPByNQfmkbRzx0hWcsuX8tgdDqEE5FQQk6XCeIU/I/4ghc3+U2m9Ake8jxsy52jvNczF0KIYW8/3NM+71P0PtmvMp56ZDOQk5Y0Nx/e983o0/OZB6KIdr7lFnAscw8/QZLOlPHj3hz73q7lnlHNbduZo/7ne+LufUQ9koqzS8+tVt78Cz1MlfZfJMyhkepn6IUvjfNfcgPPdMnsszfuOleoTyjboc+zzybQ4MQwWgc07ZUj78T/s6pw7qEn6RMYd6qnfOu9e76nP44D7vi0zX173HPa7lXtdDw+tkxTtyzu8lj/AHkDCe/q95Fj7lXuVS5sg0IiP4V8tI6uMZ58mY9mCMDZLicIefZ3Eof5xjDl/N6+ine9Q15VQfdXEv0sZpP/d7zjPaOA60eqdN5zrwGnjhIRuQ9w9lyJG61GXyN3Kf9q3bLr+9p1VX51LXyETs6lKHb6mrQIbzkTr6vnIh9fIbzwWIMntstYwEO+l55TypbYZt5//6+tyH73OuODScxCGNAr/ogjIB6HihcxzeJWPAqRC5iPydA6D6QxV0zu8tdSi3aFTDM2z+b9130qGelEwELG0uVSR9JkI6Sx4AN/VSvzW0xT4OPNFODITfrXv68b8HnrEjS8mR98oQ6tYTgfl/F12c60h56LG6y8C07juPB75Jayu7OhyD7qtj7N0mrItc6cKT5teN61k2fHjvWR/BJzjbGjA4Cvy8bZ60FBTPYa7D+3hI5uRLJee8lA4vqz533g9lVQ/1aV3f9sBn3nC827Pmge9z/NbUFvs4yKj2bl3rR/uIrreuzXnQG1GUNmpd3/ch6lfvue3n0gf+KXvr2iEf29px94imY6/PM6/InvIlc1HTKPB36tSpU6dOnQ6POvB36tSpU6dOR0Qd+Dt16tSpU6cjog78nTp16tSp0xFRB/5OnTp16tTpiKgDf6dOnTp16nRE1IG/U6dOnTp1Ohp6++3/A474MIhIUyA0AAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n \u003cp\u003eTranslations listed above (in Table 4) include \u003cem\u003ePhineas and Ferb [Fineas ve F\u0026ouml;rb], Sofia the First [Prenses Elena], Gravity Falls [Esrarengiz Kasaba], Star vs. the Forces of Evil [Star K\u0026ouml;t\u0026uuml; G\u0026uuml;\u0026ccedil;lere Karşı], PJ Masks [Pijamaskeliler], Puppy Dog Pals [Bingo ve Roli\u0026rsquo;nin Maceraları], Vampirina [Vampirina], Bluey [Bluey] and Big City Greens [Greenlerin B\u0026uuml;y\u0026uuml;kşehir Maceraları].\u003c/em\u003e Many of these cartoons, similar to TRT \u0026Ccedil;ocuk\u0026apos;s themes, center on the adventures of the main characters. They also appeal to older children by exploring themes such as invention and construction (e.g., \u003cem\u003eFineas and Ferb\u003c/em\u003e), journeys through fantastical worlds (e.g., \u003cem\u003eStar vs. the Forces of Evil\u003c/em\u003e), and superperson transformations (e.g., \u003cem\u003ePJ Masks\u003c/em\u003e).\u003c/p\u003e\n \u003cp\u003eHaving introduced the selected television series and outlined their central themes, the next step involves examining how gender representations were revealed within these cartoons. To achieve this, a systematic coding and content analysis was employed to uncover the underlying gender stereotypes, and the results were discussed statistically. In this framework, 3 episodes from each selected cartoon broadcast on TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye were analyzed, amounting to a total of 134 examples. applying a coding procedure to the dual corpus is essential for uncovering gender stereotypes. First, original corpus was coded; second, the translated corpus was coded; third, the source version was compared with the translated version to identify potential translation shifts. Finally, the examples were classified and analyzed according to the operational definitions established for the coding procedure.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Coding and Content Analysis\u003c/h2\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe content analysis method suggested by Walsh and Leaper (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) in \u0026ldquo;Content Analysis of Gender Representations in Preschool Children\u0026rsquo;s Television\u0026rdquo; and the activity categorization of Michel (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e) as suggested in \u0026ldquo;Eliminating Sexism from Children\u0026rsquo;s Literature and School Textbooks\u0026rdquo; were used for the coding procedure of this study. While the coding procedures such as the number of characters, talkativeness, speech behaviors (suggestion and support), aggressive behaviors and appearances are followed as in Walsh and Leaper\u0026rsquo;s study (2020), the activity categorization, one of the six coding procedures, was expanded following Michel\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e) study. Unlike Walsh and Leaper\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) work, some activity definitions were used on the basis of Michel\u0026rsquo;s categorization since watched episodes and transcribed cartoons largely overlapped Michel\u0026apos;s (1986) activity schema. As you will see the coding procedures in detail below, the methods of Walsh \u0026amp; Leaper (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Michel (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e) were combined for this study.\u003c/p\u003e\n \u003cp\u003eEach coded variable of the dual corpus was assessed from the perspective of gender representation. First, the operational definitions for the coding procedure were clearly defined. Second, the randomly selected three episodes for each cartoon in the dual corpus were watched and transcribed considering the operational definitions. Third, the presence of each behavior was recorded on a spreadsheet. Fourth, a sum of each variable for each gender was coded to determine the frequency of each behavior in the original and translated texts. Moreover, the source texts and dubbed versions broadcast on Disney Channel T\u0026uuml;rkiye were compared to identify gender-based speeches and gender representations interlingually. It was essential to compare the English and Turkish transcriptions of Disney-made audiovisual products during coding since it was crucial to examine whether there was a translation shift regarding gender representation while analyzing speeches on Disney Channel T\u0026uuml;rkiye, where audiovisual products undergo dubbing translation. Finally, each coding was statistically determined and interpreted separately and comparatively.\u003c/p\u003e\n \u003cp\u003eThe operational definitions for the coding procedure are detailed as follows:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe number of characters\u003c/strong\u003e in each episode of the cartoon coded the total number of female characters and male characters in a speaking role or a name. The characters\u0026apos; gender was determined by their appearance, pronouns, and names. The number of human and nonhuman characters was counted independently for each sex.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTalkativeness\u003c/strong\u003e For this study, the talking time for each gender reflected the amount of time spent by any female or male character during time-sampled intervals. The durations during which male and female characters simultaneously sang and yelled out were excluded.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSpeech behavior\u003c/strong\u003e during each episode was thoroughly watched for coding purposes, and relevant dialogues were transcribed. The operational definitions in which the dual corpus is scanned are as follows: \u003cem\u003esuggestions\u003c/em\u003e include proposals for actions not in the form of commands, whereas \u003cem\u003esupportive speeches\u003c/em\u003e include consoling, emotional advice, and encouragement on the basis of Walsh and Leaper\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) content analysis. Praise was also coded as supportive speech, reflecting their presence in the sampled television series.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAggressive behavior\u003c/strong\u003e was coded to determine whether female or male characters were more aggressive in the original and translated cartoons. According to Walsh and Leaper\u0026apos;s (2020) content analysis, aggressive behavior includes any direct or indirect acts that cause harm to others, such as insulting, hitting, social exclusion and harmful gossip. For this study, aggression is classified as verbal or nonverbal rather than direct or indirect. Aggressive behavior was coded as one variable since aggression was considered a low-occurring behavior.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eActivities\u003c/strong\u003e involve feminine-stereotyped activities (performance arts such as singing, dancing, and visual arts) and masculine-stereotyped activities (physical activities and constructive activities) in Walsh and Leaper\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) study. However, in Michel\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e, p. 51) study, the types of activities were divided into five categories: domestic tasks (e.g., cooking, food preparation), contributing to the education/well-being of children (emotional and physical support), professional activities (roles of doing work and giving orders), leisure activities (sport, art, hobbies, artistic activities, games, adventure) and political or social activities (mayor of a town, head of a club or social, artistic, cultural or sporting association). Playing with children (pretend-play) and being a role model were expanded to the category of contributing to the well-being of children, in addition to the definitions of Michel (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAppearance\u003c/strong\u003e Three components of the outlook\u0026mdash;body type, accessories, and color\u0026mdash;were used to code appearance in this research. Only human characters were coded for their body types. All human and nonhuman characters\u0026rsquo; clothes were coded for colors and accessories once for each outfit in each episode of the sampled television series.\u003c/p\u003e\n \u003cp\u003eAfter this content analysis, the gender patterns of the female and male characters were identified by comparing the original corpus of TRT \u0026Ccedil;ocuk and the translated corpus of Disney Channel T\u0026uuml;rkiye. Below, the findings will be statistically discussed.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Findings and Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBelow, findings will be presented under certain subheadings such as overall data on the number of characters, overall data on taklativeness, overall data on speech behavior, overall data on aggressive behavior, overall data on activities and overall data on appearance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverall data on the number of characters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eApproximately twice as many male characters as female characters were found in the original corpus aired on TRT \u0026Ccedil;ocuk. While the main characters were equal in number of female and male main characters for determining the data, the analysis focused on the number of characters with consistent presence across the three episodes, identifying all characters who had at least one speaking role. For instance, in the original cartoon \u003cem\u003eİbi\u003c/em\u003e, which features a female protagonist, viewing three episodes of approximately 11 minutes each revealed that 6 female and 12 male characters\u0026mdash;both human and non-human\u0026mdash;had at least one speaking role. Similarly, cartoons such as \u003cem\u003eEmiray\u003c/em\u003e [\u003cem\u003eEmiray\u003c/em\u003e], \u003cem\u003eNasreddin Hoca Zaman Yolcusu\u003c/em\u003e [Nasreddin Hodja, Time Traveler], \u003cem\u003eand Rafadan Tayfa\u003c/em\u003e [\u003cem\u003eRafadan Tayfa\u003c/em\u003e] feature male protagonists, while others like \u003cem\u003eZ Takımı\u003c/em\u003e [\u003cem\u003eTeam Z\u003c/em\u003e], \u003cem\u003eMaysa ve Bulut\u003c/em\u003e [Maysa and Bulut], \u003cem\u003eand\u003c/em\u003e Bulmaca Kulesi [\u003cem\u003ePuzzle Tower\u003c/em\u003e] include only one female character alongside at least two male characters. As a result, although the number of female and male protagonists was equal, the overall character distribution reveals a clear predominance of male figures.\u003c/p\u003e\u003cp\u003eWhen examining the translated corpus (see Table\u0026nbsp;4), it was found that among 9 selected cartoons, 4 feature female protagonists (\u003cem\u003eSofia the First, Bluey, Vampirina\u003c/em\u003e and \u003cem\u003eStar vs. the Forces of Evil)\u003c/em\u003e, 3 feature male protagonists (\u003cem\u003eFineas and Ferb, PJ Masks\u003c/em\u003e and \u003cem\u003ePuppy Dog Pals\u003c/em\u003e), and 2 have equally both female and male protagonists (\u003cem\u003eBig City Greens\u003c/em\u003e and \u003cem\u003eGravity Falls\u003c/em\u003e). For example, in \u003cem\u003eSofia the First\u003c/em\u003e, which centers on a female main character, analysis of episodes totaling 24 minutes revealed that 8 female and 9 male characters \u0026mdash;both human and non-human\u0026mdash;had at least one speaking role. This demonstrates that the presence of a female protagonist does not mean that it presents predominantly female representation. Similar result to the original corpus, the translated corpus also shows a higher number of male than female characters (Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverall Data on Talkativeness (Talking Time)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTalking time is important for portraying gender representation in cartoons. As shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e5\u003c/span\u003e, talking time of female and male characters in the original and translated TV series is presented.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTalking time of female and male characters in the original and translated television series\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eT Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSig (p)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eDisney Channel\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eT\u0026uuml;rkiye\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7,1806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10,9693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-0,528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7,7288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11,3435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eTRT \u0026Ccedil;ocuk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,1442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7,4597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-7,946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12,2454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19,0350\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhen the talking time of female and male characters was analyzed, a sex difference was observed for TRT \u0026Ccedil;ocuk (p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The data indicate that male characters have an average talking time of 12.2454\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0350 seconds, whereas female characters have an average talking time of 5.1442\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4597 seconds. It can be concluded that the talking time of male characters was greater than that of female characters, and this difference was statistically significant. For Disney Channel T\u0026uuml;rkiye, no significant difference between genders was found (p\u0026thinsp;=\u0026thinsp;0.598\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The talking times of the male and female characters were equal on this channel.\u003c/p\u003e\u003cp\u003eExamining the relationships between gender, channel and age group in terms of time is also important. Below you can see the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe relationships between gender, channel and age group in terms of time\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSig (p)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eTALKING TIME\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5,7689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8,73125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-7,385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10,9750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17,33506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eChannel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eTRT \u0026Ccedil;ocuk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9,0348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15,35041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eDisney Channel T\u0026uuml;rkiye\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7,4600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11,15291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3\u0026ndash;6 Age\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8,8412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15,13682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1,231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6\u0026ndash;11 Age\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7,8443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11,53899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhen the characters\u0026rsquo; talking times were examined, gender differences were found between the groups (p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The data revealed that male characters had an average talking time of 10.975\u0026thinsp;\u0026plusmn;\u0026thinsp;8.731 seconds, whereas female characters had an average talking time of 5.7689\u0026thinsp;\u0026plusmn;\u0026thinsp;17.335 seconds. In other words, male characters\u0026rsquo; talking time was almost twice as long as female characters\u0026rsquo; talking time was, and this difference was statistically significant. Channel-specific analysis revealed that the average talking time on TRT \u0026Ccedil;ocuk Channel was 9.0348\u0026thinsp;\u0026plusmn;\u0026thinsp;15.350 seconds, whereas the average talking time on Disney Channel T\u0026uuml;rkiye was 7.4600\u0026thinsp;\u0026plusmn;\u0026thinsp;11.153 seconds. This indicates that characters on the TRT \u0026Ccedil;ocuk generally talked for longer, and this difference was statistically significant. These quantitative findings are consistent with a broader discussion of gender stereotypes in audiovisual products for children. The portrayal of boys talking more than girls in cartoons, whether in original or translated versions, may lead young viewers to internalize the idea that conversational dominance is a natural male characteristic. Leaper (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) claims that variations in talking time may reflect speakers\u0026rsquo; dominance through their control of conversation. From this perspective, a child receiver who models gender roles from on-screen portrayals may come to perceive it as normal for boys to talk more and dominate conversations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverall Data on Speech Behavior\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpeech behavior is presented in two separate tables, categorized as supportive speech or suggestion. In table 7, the statistical results of the distribution of supportive speech by female and male characters are shown.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable\u0026nbsp;7\u003c/b\u003e Comparing the use of supportive speech in TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye\u003c/p\u003e\u003cp\u003eaccording to gender\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eSupportive Speech-Female\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue Label\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRT \u0026Ccedil;ocuk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,2258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,08925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13,304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDisney Channel TR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,4167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,15386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e59,295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSupportive Speech-Male\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eValue Label\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e63 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTRT \u0026Ccedil;ocuk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,2097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,10141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8,696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0,006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e27 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisney Channel TR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,4583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,15801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e63,030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eConsidering the use of supportive speech in the original cartoons in TRT \u0026Ccedil;ocuk, although no clear difference in female characters\u0026rsquo; (0,2258) use of supportive speech and male characters\u0026rsquo; (0,2097) use of supportive speech is apparent at first glance, the numerical difference is significant. Given that female characters are represented by supportive speeches within the framework of traditional gender norms, this finding suggests that TRT \u0026Ccedil;ocuk content aligns with traditional portrayals of femininity, which often emphasize encouragement, cooperation, and emotional support. This is consistent with von Flotow\u0026rsquo;s (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) idea that gender-focused work highlights how language reflects power relations between the sexes and how women\u0026rsquo;s roles are often socially constructed through language practices (p.122). For example, in the original cartoon \u003cem\u003eAslan\u003c/em\u003e [Aslan], a female character is depicted praising a male character\u0026rsquo;s idea, while in \u003cem\u003eAkıllı Tavşan Momo [The Smart Rabbit Momo]\u003c/em\u003e, a female character takes on a nurturing role by comforting her friend (for more examples, see Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such patterns may subtly reinforce to child audiences that women are expected to validate and nurture others, particularly male counterparts.\u003c/p\u003e\u003cp\u003eWhen examining supportive speeches in Disney Channel T\u0026uuml;rkiye as the translated corpus, a different pattern emerges: no significant difference was observed between female characters\u0026rsquo; (0,4167) and male characters\u0026rsquo; (0,4583) use of supportive speech. This relative balance offers that the translated content may depart from the conventional gendered speech stereotypes seen in the original TRT \u0026Ccedil;ocuk cartoons. For instance, in \u003cem\u003eFineas and Ferb\u003c/em\u003e, both female and male characters\u0026rsquo; speeches can be equally supportive and have been translated faithfully: In the \u0026lsquo;Rollercoaster\u0026rsquo; episode, the speech of a female character, \"It really was the coolest coaster ever. You guys make a great team\" is rendered as \"Bu yaptığın harikaydı, Fineas. Bindiğim en muhteşem hız treniydi.\" Likewise, in the \u0026lsquo;Tourist Trapped\u0026rsquo; episode of \u003cem\u003eGravity Falls\u003c/em\u003e, a supportive speech of a male character, \"Don\u0026rsquo;t worry Mabel, I will save you from the zombie!\" is translated faithfully as \"Merak etme Mabel, seni o zombilerden kurtaracağım!\" Instead of reinforcing a clear-cut association between supportive speech and femininity, the translations present supportive language as a more gender-neutral way. While this does not necessarily indicate a complete break from traditional representations, it may expose child receivers to a slightly more egalitarian model of communicative behavior, in which encouragement and consolidation are not framed as predominantly female traits.\u003c/p\u003e\u003cp\u003eSuggestion is another speech behavior. In Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e8\u003c/span\u003e, you can see the statistical results showing the distribution of suggestion speech behavior by female and male characters.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparing the use of suggestions in TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye according to gender \u003cb\u003eSuggestion-Female\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eValue Label\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e63 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTRT \u0026Ccedil;ocuk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,6290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,14051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32,081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e27 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisney Channel TR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,4167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,15386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e105,194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eSuggestion-Male\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eValue Label\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e63 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTRT \u0026Ccedil;ocuk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,8548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,36629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42,988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e27 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisney Channel TR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,4833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,11801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e103,076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAs Walsh and Leaper (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted, previous studies have shown that boys are more likely to offer suggestions than girls (p. 23). This pattern is clearly reflected in TRT \u0026Ccedil;ocuk (original corpus), where male characters present significantly more suggestion behavior (1,8548) than female characters (0,6290). In contrast, Disney Channel T\u0026uuml;rkiye showed no notable gender difference, with comparable scores for males (0,4833) and females (0,4167). Male-led shows such as \u003cem\u003eAslan\u003c/em\u003e [Aslan], E\u003cem\u003emiray\u003c/em\u003e [\u003cem\u003eEmiray\u003c/em\u003e], \u003cem\u003eand Rafadan Tayfa\u003c/em\u003e [\u003cem\u003eRafadan Tayfa\u003c/em\u003e] on TRT \u0026Ccedil;ocuk featured more male characters, who dominated speech and actions. For example, in \u003cem\u003eAslan\u003c/em\u003e, the male protagonist consistently leads and makes more suggestions, reinforcing male-centered narratives in the original corpus (for more examples, see Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings suggest that the original TRT \u0026Ccedil;ocuk content often associates suggestion-making, a form of verbal initiative closely linked to decision-making and authority, with male characters.\u003c/p\u003e\u003cp\u003eWhen the translated corpus is examined in comparison with the source text, it presents statistically almost equally gender representation. For example, in the episode \u0026lsquo;Catboy\u0026rsquo;s Cloudy Crisis\u0026rsquo; of \u003cem\u003ePJ Masks\u003c/em\u003e, it is observed that suggestions from both genders were translated faithfully to the source text: The male character\u0026rsquo;s speech \"We need to find out where these clouds are coming from\" is rendered literally as \"Bulutların nereden geldiğini \u0026ouml;ğrenmemiz gerekiyor\", while the female character\u0026rsquo;s suggestion \"Let\u0026rsquo;s follow the hose\" is likewise faithfully dubbed as \"Hortumu takip edelim.\"\u003c/p\u003e\u003cp\u003eIn terms of the statistical finding of suggestion overall, Disney Channel T\u0026uuml;rkiye (translated corpus) potentially offers a more egalitarian distribution of verbal leadership. This divergence could be the result of different source material norms, translation practices that downplay gendered speech patterns, or the influence of more globally oriented storytelling in Disney productions. For young audiences, such differences in representation matter: while TRT \u0026Ccedil;ocuk may implicitly signal that leadership and initiative are male domains, Disney Channel T\u0026uuml;rkiye\u0026rsquo;s portrayals could normalize the idea that both boys and girls can take active roles in guiding interaction and decision-making. The table below presents the statistical differences in aggressive behavior showing the distribution by female and male characters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverall Data on Aggressive Behavior\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAggressive behavior is presented in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e9\u003c/span\u003e, with the statistical results showing the distribution of aggression by female and male characters.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences in aggressive behavior according to gender \u003cb\u003eAggressive behavior - Female\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue Label\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSig (p)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRT \u0026Ccedil;ocuk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDisney Channel TR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e,4167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e,65386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eAggressive behavior - Male\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue Label\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSig (p)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRT \u0026Ccedil;ocuk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0,428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 episodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDisney Channel TR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e,4583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e,65801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll p values were greater than 0.05 when the levels of male and female aggression for TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye were compared, indicating no statistically significant difference between the two variables. In previous studies, it was observed that aggression was more prevalent in male representations (Ercen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the frequency counts in the present study show a similar tendency. In the original corpus, only one instance of aggressive behavior was observed, and it was represented by a male character In TRT \u0026Ccedil;ocuk. This instance involves a threatening statement by a male character in the \u003cem\u003eEmiray\u003c/em\u003e episode titled \u003cem\u003eBuğdayları Kim Yere D\u0026ouml;kt\u0026uuml;?\u003c/em\u003e [Who Spilled the Wheat on the Ground?]. When comparing aggression between female and male characters in the translated corpus, a far greater number of depictions of aggression by male characters than female characters were observed. Aggressive behaviors were predominantly found in cartoons aimed at older children, such as \u003cem\u003eBig City Greens\u003c/em\u003e, \u003cem\u003eGravity Falls\u003c/em\u003e, and \u003cem\u003eStar vs. The Forces of Evil\u003c/em\u003e. For instance, \u003cem\u003eBig City Greens\u003c/em\u003e features a fictional rejection scene involving a female character, which may subtly reinforce gender-based power dynamics and influence children's perceptions of how girls are treated in social interactions. Although it depicts an imaginary scenario, this scene\u0026mdash;where a male character sarcastically rejects a girl\u0026rsquo;s proposal and then kicks her\u0026mdash;is still conveyed to the child audience through translation (for more examples, see Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such portrayals, even when fantastical, can implicitly model forms of male dominance and normalization of physical or verbal aggression toward female characters.\u003c/p\u003e\u003cp\u003eThis stark difference between original and translated suggests that the original may consciously avoid aggressive behavior, especially physical aggression, in their narratives, possibly due to local broadcasting standards, cultural expectations about age-appropriate content, or a greater emphasis on promoting prosocial interactions. Disney Channel T\u0026uuml;rkiye, on the other hand, particularly in programming for older children, may reflect both the nature of the source material and different cultural thresholds for acceptable depictions of interpersonal conflict. As a result, in the rare examples where aggression does occur, it is consistently represented by male characters, while female characters appear in more submissive roles. Across both the original and translated cartoons, a recurring pattern emerges: aggression is exclusively associated with male characters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverall Data on Activities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSince the data come from two different channels, the activities coded in the table are categorized as channel 1 and 2. The Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e10\u003c/span\u003e examines the relationship between channels, age groups, and activities.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSimilarities and differences in activities according to channel and age\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChi-Squre Tests\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSig (p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCorrelation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCor (Sig)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomestic Tasks 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomestic Tasks 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContributing Education 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24,593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContributing Education 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional Activities 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30,741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional Activities 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeisure Activities 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51,954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeisure Activities 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;6 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomestic task 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,805\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;11 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomestic task 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,835\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;6 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContributing Education 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;11 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContributing Education 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,972\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;6 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional activities 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;11 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional activities 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;6 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeisure Activities 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21,955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,864\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;11 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeisure Activities 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29,722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA comparison of domestic tasks, educational contributions, professional activities, and leisure activities across channels and age groups reveals significant correlations: According to the statistic findings, domestic task representation does not differ significantly between the 3\u0026ndash;6 and 6\u0026ndash;11 age groups, but domestic tasks (0.000) vary strongly by channel, with TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye showing different patterns of how often domestic chores are assigned to characters. Female characters were more often shown performing domestic tasks, especially in the original TRT \u0026Ccedil;ocuk corpus, while such representations were rare in the translated content of Disney Channel T\u0026uuml;rkiye. For instance, in local cartoons such as \u003cem\u003eAkıllı Tavşan Momo [The Smart Rabbit Momo], Elif\u0026rsquo;in D\u0026uuml;şleri [Elif\u0026rsquo;s Dreams]\u003c/em\u003e, Maysa ve Bulut \u003cem\u003e[Maysa and Bulut]\u003c/em\u003e, and \u003cem\u003eNiloya\u003c/em\u003e, female characters are most often portrayed cooking. A notable example appears in the \u003cem\u003eBayram [Festival]\u003c/em\u003e episode of \u003cem\u003eElif\u0026rsquo;in D\u0026uuml;şleri [Elif\u0026rsquo;s Dreams]\u003c/em\u003e, where the mother invites her daughter to wrap grape leaves, a scene that implicitly frames cooking as a feminine domestic task.\u003c/p\u003e\u003cp\u003eAs the comparison of contributing children\u0026rsquo;s education especially being a role model by adults, differences come from channel, not age group. Correlations (up to \u0026minus;\u0026thinsp;0.724, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) indicate that channels portray educational contribution very differently. Likely TRT \u0026Ccedil;ocuk emphasizes more contributing education content, while Disney Channel T\u0026uuml;rkiye portrays less on this aspect.\u003c/p\u003e\u003cp\u003eAs the findings present, professional activity differences are due to channel, not age. Correlations (\u0026ndash;0.732 to \u0026minus;\u0026thinsp;0.909, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) again show a channel-based divergence: Occupational roles across both corpora also revealed persistent gender representations. In the original TRT \u0026Ccedil;ocuk cartoons, positions such as newscasters, salespersons and some authoritative roles were displayed as masculine-stereotyped activities. Similarly, in the translated cartoons, male characters were more often depicted in traditionally masculine-stereotyped occupations, including weathermen, CEOs, mayors, and school principals. In contrast to these masculine-stereotypical activities, an analysis of the 63 episodes in the original corpus revealed that only one episode of the cartoon \u003cem\u003eZ Takımı\u003c/em\u003e [\u003cem\u003eTeam Z\u003c/em\u003e], included a female newscaster. Likewise, out of the 27 episodes in Disney Channel T\u0026uuml;rkiye, only one episode of \u003cem\u003eBig City Greeens\u003c/em\u003e had a female reporter. Although such representations are rare, the presence of these exceptions is not insignificant. The portrayals of women in professional or authoritative roles can act as deconstructing stereotypes, offering children alternative models of what women can do in both the public and professional spheres (Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe findings present that no age-related differences, only channel-based. Considering the category of leisure time activities in the original corpus, male characters were nearly twice as represented in adventure and sports-related activities as female characters such as \u003cem\u003eİbi, Nasreddin Hoca Zaman Yolcusu [Nasreddin Hodja, Time Traveler], Rafadan Tayfa [Rafadan Tayfa]\u003c/em\u003e and \u003cem\u003eZ Takımı [Team Z\u003c/em\u003e]. Similar representations are also evident in the translated corpus. As seen in \u003cem\u003eSofia the First\u003c/em\u003e, boys are actively involved in activities like the Flying Derby, while girls are passively portrayed as spectators. In the episode \u003cem\u003e\u0026lsquo;Just One of the Princes\u0026rsquo;\u003c/em\u003e of \u003cem\u003eSofia the First\u003c/em\u003e, Sofia expresses her desire to participate in the Flying Derby. Her stepsister Amber, however, attempts to dissuade her, remarking that \"Flying Derby isn\u0026rsquo;t a princess thing.\" This exchange exemplifies how certain leisure activities, particularly sports, are framed as male domains and, through translation, reinforce gendered lessons for child audiences. Similar patterns appear in the cartoons such as \u003cem\u003eStar vs. The Forces of The Evil\u003c/em\u003e and \u003cem\u003eFineas and Ferb\u003c/em\u003e where girls are shown completing the task/work, yet it is the boys who are positioned as the ones giving the orders (for more examples, see Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such representations also enhance traditional divisions between the active, authoritative male and the passive, task-oriented female, thereby normalizing unequal gender dynamics for child audiences.\u003c/p\u003e\u003cp\u003eThe translated corpus of Disney Channel T\u0026uuml;rkiye, by contrast, showed no discernible gender differences. Although male characters were frequently portrayed as strong, active, and authoritative figures teaching or guiding others, there were notable exceptions that disrupted this pattern. For example, in \u003cem\u003eBluey\u003c/em\u003e, the father character actively participates in his daughters\u0026rsquo; games and playfully engaging with them (for more examples, see Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This type of example deconstructs stereotypical gender roles and provides positive gender lessons to the dual address (Alvstad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo sum up, channel is the main factor shaping activities (domestic, educational, professional, leisure). TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye present systematically different distributions of what characters do. Age group (3\u0026ndash;6 vs. 6\u0026ndash;11) does not significantly affect representation in these categories. In other words, the channels do not tailor domestic tasks, education, professional, or leisure portrayals differently depending on whether the content is aimed at younger or older children.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverall Data on Appearance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBelow in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e11\u003c/span\u003e one can see comparison of gender, color and physical appearance according to channel and age groups.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of gender, color and physical appearance according to channel and age groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChi-Square Tests\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSig (p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCorrelation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCor (Sig)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34,776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBody Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e257,344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29,685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBody Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116,444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn terms of physical appearance, Turkish children are exposed to similar gender portrayals in the dual corpus. Female characters are more likely to have a feminine-stereotyped color (pink), whereas male characters are more likely than female characters to have a masculine-stereotyped color (blue). These color codings function as visual shorthand for gender identity, making gender instantly recognizable to even the youngest viewers. A similar pattern was observed in the depiction of accessories: bows, hairpins, and makeup are frequently used to mark female characters, even in the case of nonhuman characters such as \u003cem\u003eAkıllı Tavşan Momo [The Smart Rabbit Momo], Ciciki [Ciciki], Doru [Doru], Puppy Dog Pals [Bingo ve Rolly\u0026rsquo;nin Arkadaşları]\u003c/em\u003e (for more examples, see Y\u0026uuml;cel Din\u0026ccedil;, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such practices illustrate how gender is not only embedded in human characters\u0026rsquo; design but also imposed on anthropomorphic figures, thereby enhancing gender binaries through visual cues.\u003c/p\u003e\u003cp\u003eWhen the colors that represent female and male characters according to channels were compared, there was no difference between the same gender (p\u0026thinsp;=\u0026thinsp;0,392\u0026thinsp;\u0026gt;\u0026thinsp;0,05) or color usage (p\u0026thinsp;=\u0026thinsp;0.143\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but a significant difference was found when gender and color usage were compared (p\u0026thinsp;=\u0026thinsp;0.025\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For masculine depictions, blue is rated highest, followed by green in second place and gray in third place. With respect to female representation, pink is the first, blue is the second, and white is the third.\u003c/p\u003e\u003cp\u003eOne notable divergence from stereotypical norms is the portrayal of body type. In contrast to stereotypical body types, female characters are observed as average weight and height, similar to male characters, in the selected cartoons. This absence of extreme body ideals can be viewed as a positive departure from the hyper-feminized body shapes. Nonetheless, the persistence of gendered colors and accessories suggests that while certain stereotypes, such as unrealistic body proportions, are avoided, other subtler markers of gender difference remain deeply entrenched. These visual patterns, particularly when reinforced across both domestic and imported productions, may contribute to early internalization of gender codes, teaching children to read color and accessory use as natural indicators of femininity or masculinity.\u003c/p\u003e\u003cp\u003eTo sum up from translation studies perspective, it was observed that the translations remained largely faithful to the source material. While aggression and activities were translated accurately, the clearest examples of this fidelity were in speech behavior. This suggests that factors beyond the translator, such as institutional or editorial gatekeepers, have a significant influence on these representations.\u003c/p\u003e\u003cp\u003eRegarding the study\u0026rsquo;s limitations, 1the analysis was based on 34 examples from two channels covering the period between 2015 and 2021. The study tried to present gender representation. For further studies, how children actually interpret or internalize gender lessons through original and translated audiovisual products can be a topic. Moreover, translation shifts identified between originals and translated versions may reflect technical constraints or production decisions rather than deliberate ideological choices. These considerations highlight opportunities for further interdisciplinary research that bridges translation studies, media studies, and early child education.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study examined gender representation in both original (TRT \u0026Ccedil;ocuk) and translated (Disney Channel T\u0026uuml;rkiye) cartoons through a content analysis framework provided by Walsh and Leaper (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Michel\u0026rsquo;s (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) activity categorization. Across the dual corpus, the coding results do not significantly differ in terms of the number of characters, talking time or appearance between the original and translated corpus. However, meaningful variations emerged in speech behavior, aggressive behavior and activity types, as detailed in the findings.\u003c/p\u003e\u003cp\u003eFrom the perspective of translation studies, these results suggest that translated content does not necessarily introduce foreign gender values or broaden children's perspectives. Instead, it often mirrors local gender norms due to deliberate selection practices and cultural adaptation. In this context, translation does not act as a transformative force for gender awareness among children but tends to reproduce familiar, socially accepted gender portrayals. When translated audiovisual products are exposed to such a gatekeeping, such as institutional policies, broadcaster preferences, and deliberate selection of suitable imports, gender portrayals are filtered to align with familiar cultural scripts. This process limits the potential of translation to disrupt established gender schemas and instead reinforces the cognitive frameworks that children already hold. In other words, translation becomes less of a transformative cultural bridge and more of a cultural mirror, reflecting rather than reshaping the gender norms of the receiving context. While audiovisual translation holds the capacity to broaden young viewers\u0026rsquo; perspectives and expose them to diverse gender roles, its current implementation in children\u0026rsquo;s programs tends to maintain rather than challenge conventional portrayals.\u003c/p\u003e\u003cp\u003eFuture research could dive into the dynamics of this gatekeeping process, focusing on how institutional and editorial decisions determine the types of gender narratives that are imported, adapted, or omitted. Comparative studies could also explore audiovisual products designed for a dual address, assessing whether content aimed at both children and adults offers greater scope for challenging gender norms. Finally, examining domestic remakes and adaptations might also reveal how local production practices negotiate between global storytelling trends and national cultural expectations. Such interdisciplinary inquiry\u0026mdash;linking translation studies, gender theory, and media\u0026mdash;can deepen our understanding of how translated audiovisual products participate in the ongoing socialization of gender in globalized childhoods.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.Y.D. wrote the main manuscript. S.S.B. gave feedback at the each part and contributed to develop the main study. Finally, all authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed during the current study are available in the Hacettepe University repository, https://openaccess.hacettepe.edu.tr/items/befb9107-1098-4571-bbb5-c54a225c2429. All data generated during this study are included in this published thesis. The datasets analysed during the current study are also publicly available in the ResearchGate link https://www.researchgate.net/publication/378550255_GENDER_REPRESENTATIONS_IN_TRANSLATED_AND_ORIGINAL_CARTOONS_A_TURKISH-ENGLISH_DUAL_CORPUS_COMPARATIVE_ANALYSIS\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlvstad C (2012) Children's literature and translation. Handbook of Translation Studies: Volume 1. 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Gazi \u0026Uuml;niversitesi, T\u0026uuml;rkiye\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemirhan Handeg\u0026uuml;l (2017) A game of genders: the development of translated feminist children's literature in 21st century Turkey. \u003cem\u003eEdinburgh: The University of Edinburgh. Un-published MSc thesis.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemirhan Handeg\u0026uuml;l (2020) Children's literature, feminism, adaptation, and translation. The Routledge Handbook of Translation, Feminism and Gender. Routledge, pp 528\u0026ndash;539\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemirhan Handeg\u0026uuml;l (2022) Translating gender and sexuality in children's literature. Turkey as a case study\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Giovanni E (2010) Shifts in audiovisual translation for children: Reviving linguistic-driven analyses. \u003cem\u003e\u0026Eacute;crire et traduire pour les enfants. Voix, images et mots. Bruxelles: Peter Lang\u003c/em\u003e, 303\u0026ndash;320\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDin\u0026ccedil;kan Yeşim (2017) \u0026Ccedil;ocuk Yazınında Sessel \u0026Ouml;gelerin \u0026Ccedil;evirisinin \u0026Ouml;nemi. \u0026Ccedil;eviribilim ve Uygulamaları 23:55\u0026ndash;69\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDisney Channel T\u0026uuml;rkiye (n.d.) \u003cem\u003eHome\u003c/em\u003e [YouTube channel]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.youtube.com/channel/UCiTtqNjfg2jLH2uraXbnwow\u003c/span\u003e\u003cspan address=\"https://www.youtube.com/channel/UCiTtqNjfg2jLH2uraXbnwow\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErcen R, \u0026Ccedil;ağlar et al (2022) 3\u0026ndash;6 Yaş \u0026ccedil;ocuklarının izlediği \u0026ccedil;izgi filmlerdeki kız erkek karakterlerin şiddet ve saldırganlık eğilimlerinin incelenmesi. 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Buletin Stiintific seria Fascicula Filologie 26(1):373\u0026ndash;381\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTRT (2023) Tarih\u0026ccedil;e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trt.net.tr/kurumsal/tarihce\u003c/span\u003e\u003cspan address=\"https://www.trt.net.tr/kurumsal/tarihce\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 31 October 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTRT \u0026Ccedil;ocuk (2023) Hakkımızda. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trtcocuk.net.tr/\u003c/span\u003e\u003cspan address=\"https://www.trtcocuk.net.tr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 31 October 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTRT \u0026Ccedil;ocuk (2023) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trtcocuk.net.tr/yayin-akisi/\u003c/span\u003e\u003cspan address=\"https://www.trtcocuk.net.tr/yayin-akisi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 31 October 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTRT \u0026Ccedil;ocuk (2023) (n.d.) \u003cem\u003eHome\u003c/em\u003e [YouTube channel]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.youtube.com/@trtcocuk\u003c/span\u003e\u003cspan address=\"https://www.youtube.com/@trtcocuk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 31 October\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Coillie J, McMartin J (2020) Children\u0026rsquo;s Literature in Translation: Texts and Contexts. Leuven University, p 280\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarga C (2012) Childish translation vs. translation for children. The subtitling of fictional dialogues in cartoon movies. Translating fictional dialogue Child young people, 357\u0026ndash;376\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalsh A, Leaper C (2020) A content analysis of gender representations in preschool children's television. Mass Communication Soc 23(3):331\u0026ndash;355\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTİAK (2023) Televizyon İzleme Araştırmaları Anonim Şirketi. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tiak.com.tr/tablolar\u003c/span\u003e\u003cspan address=\"https://tiak.com.tr/tablolar\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 31 October 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eY\u0026uuml;cel Din\u0026ccedil;, Fatma (2024) Gender Representations in Translated and Original Cartoons: A Turkish-English Dual Corpus Comparative Analysis. M.A. Thesis, Hacettepe University\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZoli M (2023) Translating for Children: Translation and Analysis of Nathan A. Jones's Bogamus the Troll. Web. 05 Aug 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e This article is a revised version of part of the master\u0026rsquo;s thesis titled \u003cem\u003eGender Representations in Translated and Original Cartoons: A Turkish-English Dual Corpus Comparative Analysis\u003c/em\u003e (2024, Hacettepe University, Ankara)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e See discussions of interventions in translating children\u0026rsquo;s literature and the moral or didactic motivations behind them (e.g., McMartin and Van Coillie, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pascua-Febles, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e among many others)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Whole Day: Starting at 02:00 on the measurement day and continuing until the same time the next day,\u003c/span\u003e\u003cdiv id=\"Par30\" class=\"Para\"\u003ecovering the 24-hour period. PT (Prime Time): The 3-hour period during which TV viewing is highest, defined as between 20:00 and 23:00. OPT (Off Prime Time): The time period between 07:00 and 20:00. (The data was derived from the website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tiak.com.tr/tablolar\u003c/span\u003e\u003cspan address=\"https://tiak.com.tr/tablolar\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.)\u003c/div\u003e\u003cdiv id=\"Par31\" class=\"Para\"\u003eFunding: Not applicable\u003c/div\u003e\u003cdiv id=\"Par32\" class=\"Para\"\u003eDATA AVAILABILITY STATEMENT\u003c/div\u003e\u003cdiv id=\"Par33\" class=\"Para\"\u003eThe datasets analysed during the current study are available in the Hacettepe University repository, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openaccess.hacettepe.edu.tr/items/befb9107-1098-4571-bbb5-c54a225c2429\u003c/span\u003e\u003cspan address=\"https://openaccess.hacettepe.edu.tr/items/befb9107-1098-4571-bbb5-c54a225c2429\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. All data generated during this study are included in this published thesis. The datasets analysed during the current study are also publicly available in the ResearchGate link \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/378550255_GENDER_REPRESENTATIONS_IN_TRANSLATED_AND_ORIGINAL_CARTOONS_A_TURKISH-ENGLISH_DUAL_CORPUS_COMPARATIVE_ANALYSIS\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/378550255_GENDER_REPRESENTATIONS_IN_TRANSLATED_AND_ORIGINAL_CARTOONS_A_TURKISH-ENGLISH_DUAL_CORPUS_COMPARATIVE_ANALYSIS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e \u003cdiv id=\"Par34\" class=\"Para\"\u003eETHICAL APPROVAL\u003c/div\u003e \u003cdiv id=\"Par35\" class=\"Para\"\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/div\u003e\u003cdiv id=\"Par36\" class=\"Para\"\u003eINFORMED CONSENT This article does not contain any studies with human participants performed by any of the authors.\u003c/div\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gender representation, corpus-based translation studies, audiovisual translation, translating for children, cartoons, content analysis","lastPublishedDoi":"10.21203/rs.3.rs-7573349/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7573349/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on gender representations of the cartoons at the intersection of audiovisual translation and translating for children. In parallel with the expansion of audiovisual translation, the growing diversity and accessibility of gender representations have brought into the question of what is being presented to children. Both original and translated audiovisual products teach certain gender roles to the child receiver, who is exposed to the pervasive nature of media consumption. Adopting the perspective of a corpus-based study in translation studies, this study investigates gender representations in original and translated cartoons on two widely viewed and preferred digital TV channels broadcasting in T\u0026uuml;rkiye, namely TRT \u0026Ccedil;ocuk and Disney Channel T\u0026uuml;rkiye. With a target child receiver age range of 3\u0026ndash;11, thirty cartoons aired on these digital TV channels between 2015 and 2021 were selected as the dual corpus of this study. The basic surmise is that children of the studied age are subjected to both types of products, taking in gender identity roles from two sources, produced in multiple different cultures. So, the aim is to identify what kind of gender roles children are exposed to through audiovisual products and whether there are any similar or conflicting images and messages. The method proposed by Abigail Walsh \u0026amp; Campbell Leaper (2020) and the activity categorization of Andr\u0026eacute;e Michel (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) have been employed to identify gender representations on the original cartoons on the TRT \u0026Ccedil;ocuk channel and the translated foreign cartoons of Disney Channel T\u0026uuml;rkiye. The analysis yielded relative rates on the basis of the following factors: the number of characters, talkativeness, speech behavior (support or suggestion), aggressive behavior, activities, and appearance. In conclusion, this study reveals that both the original and translated audiovisual products perpetuate numerous gender stereotypes, with limited variation in character number, appearance, or talking time, but notable differences in speech behavior and activity patterns. Institutional and editorial gatekeeping in translation choices reinforces existing gender schemas rather than broadening children\u0026rsquo;s exposure to diverse gender roles.\u003c/p\u003e","manuscriptTitle":"Gender Representations in Original and Translated Audiovisual Products for Children in Turkish: A Corpus- based Study i","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 18:05:21","doi":"10.21203/rs.3.rs-7573349/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1888ed09-b83c-4e15-9730-6cdc63c21891","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57663567,"name":"Humanities/Cultural and media studies"},{"id":57663568,"name":"Social science/Cultural and media studies"},{"id":57663569,"name":"Biological sciences/Psychology"},{"id":57663570,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-06T21:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 18:05:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7573349","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7573349","identity":"rs-7573349","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}