Attitude of Chinese and American media on China’s poverty alleviation based on Appraisal approach

Attitude of Chinese and American media on China’s poverty alleviation based on Appraisal approach | 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 Attitude of Chinese and American media on China’s poverty alleviation based on Appraisal approach Yi Wei, Lihua Liu, Luhan Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7544117/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 aims to describe and explain the attitude distribution in news discourses about China’s poverty alleviation practice in Chinese and American mainstream media with the framework of appraisal approach. The analysis reveals that Chinese media adopts a positive stance primarily employing Appreciation resources, while American media takes a negative perspective relying on Judgement resources. The different distribution of attitudinal meaning is manifested in the evaluation on the four categories of poverty alleviation, that is, implementors, actions, recipients, and outcomes, with Chinese media focusing on actions and American media on implementors and recipients. The attitudinal differences can be traced back to differing political ideologies, sociocultural values, and communication strategies between these two countries. This research contributes to offering insights into how media discourse constructs and reflects sociopolitical realities, influencing international perceptions and intercultural communication. Humanities/Cultural and media studies Social science/Cultural and media studies Humanities/Language and linguistics Social science/Language and linguistics Social science/Politics and international relations Appraisal approach attitude news discourse Chinese media American media poverty alleviation Figures Figure 1 Figure 2 1. Introduction Poverty is one of the greatest challenges threatening the development of human society. Since the 18th National Congress of the Communist Party of China in 2017 set the goal of building a moderately prosperous society in all respects, China has given high priorities to poverty eradication on the agenda and launched a vigorous battle against poverty. On February, 25th, 2021, a grand gathering ceremony was held to mark China’s accomplishments in poverty alleviation and honor model poverty fighters 1 . Through the poverty alleviation project, it is claimed in the official report that China has completely eradicated extreme poverty, making an important contribution to the cause of global poverty alleviation. From 2012 to 2020, 98.99 million impoverished rural residents living under the current poverty line were lifted out of poverty. Also, 832 impoverished counties and 128,000 villages were removed from the nation’s poverty list during that period. The livelihood of impoverished residents has been dramatically improved in terms of medical care, education, housing and drinking water. The annual amount of disposable income per capita for rural residents in impoverished areas increased from 6,079 yuan in 2013 to 12,588 yuan in 2020. 2 As one of the social practices in China, poverty alleviation holds significance not only within its own context but also on a global scale, for it might provide poverty alleviation experience for developing countries. However, the social reality is usually constructed by via discourses, and the same social practice might be represented quite differently in different discourses. It thus seems meaningful to examine the ways in which international media’s representation about China and its poverty alleviation. Following this assumption, this study, drawing upon the appraisal approach in systemic functional linguistics (Martin & White, 2005 ), intends to compare the ways in which China and its poverty alleviation are evaluated in Chinese and American news coverages. Specifically, this study aims to answer the following questions: (1) What are the distributional features of attitude resources in the Chinese and American news reports? (2) What are the distributional features of attitude resources towards specific appraised entities in the Chinese and American news reports? (3) And what are the underlying motivations that contribute to the differences? 2. News discourse concerning China’s poverty alleviation In news discourse studies, a well-documented body of research explores news discourse related to China’s poverty alleviation practices, but these studies predominantly approach the subject from the perspective of international communication instead of the linguistic perspective. In the field of international communication, researches often examine how overseas media construct issues and establish the reporting agenda for China’s poverty alleviation efforts. For instance, from the perspective of the agenda-setting framing, Shi and Wang ( 2019 ) discovered that in The New York Times ’ reporting, the topics of China’s poverty alleviation and human rights present a “fragmented framing”; and the role of poverty alleviation in China’s human rights progress has been weakened, thus constructing a negative image of China in the discourse of “human rights”. Similarly, Huang ( 2020 ) found that the global significance of China’s poverty alleviation has been obscured in the international media, and the political motives behind poverty alleviation are excessively emphasized; external factors such as global trade are highlighted for their role in China’s poverty reduction, while the efforts of the Chinese government itself are overlooked. A prevailing view within Chinese academia attributes Western media’s imbalanced coverage of China to deeply entrenched ideological predispositions. In response to the biases and stereotypes propagated by western media, Chinese scholars have made efforts to offer insights that can contribute to the refinement of China’s international discourse model, which might enable China to effectively articulate China’s own positive narratives about the poverty alleviation practice. Yang and Qiu ( 2021 ) conducted a study on the topic selection and communication effectiveness of poverty alleviation by China Daily on Twitter, and found that the most effective types of topics include those of international poverty alleviation, human development capability, and green development. In terms of the specific narrative approaches, the research indicates that the use of individual narratives and visualizations can bring about positive communication effects, while there is a lack of correlation between data listing and communication effectiveness. Compared with above studies about narrative model, research by Wang and Zhang ( 2020 ) focuses on how to eliminate the western media’s skepticism towards China’s poverty alleviation. They found that western media’s skepticism primarily stems from disparities in values, ideologies, and institutional mechanisms between China and the western world. More specifically, this skepticism can be attributed to three main factors: ideological biases, divergent poverty alleviation systems, and challenges in effectively bridging the narrative gap. Addressing these raised concerns, they proposed that it is necessary for China to prioritize intercultural communication and exchange, diminish the ideological undertones in its foreign reports, and enhance the construction of China’s international discourse model concerning poverty alleviation. As is summarized, however, little attention has been directed towards descriptive research that starts from a discourse perspective to explore the attitude meaning employed in news texts. Firstly, there is a noticeable gap in the exploration of news topics related to China’s poverty alleviation from a linguistic perspective, particularly through the application of appraisal approach. Secondly, on the topic of China’s poverty alleviation, comparative analyses of Chinese and foreign media coverage remain understudied, yet are essential for examining potential discrepancies between China’s intended international narratives and global audiences’ actual perceptions of its developmental initiatives. Thirdly, the discourse interaction surrounding China’s poverty alleviation serves as a paradigmatic case of intercultural communication between Eastern and Western civilizations. Systematic examination of such interactions provides critical insights for bridging ideological divides and fostering deep understanding. 3. Methodology 3.1 Corpus and data collection Two comparative corpora composed of English news texts regarding China’s poverty alleviation respectively from Chinese and American media are compiled. The Chinese corpus, termed as Chinese Poverty Alleviation News Corpus (CPANC), is retrieved from Chinese mainstream media — China Daily , Xinhua Agency , Global Times , and CGTN . The American corpus, entitled as American Poverty Alleviation News Corpus (APANC), is collected from American mainstream media— the New York Times , Cable News Network (CNN) , Bloomberg , and Voice of America (VOA) . The online newspaper database LexisNexis (from which the news texts of New York Times , CNN , Bloomberg , Xinhua Agency are retrieved) along with the websites of newspaper (from which the news texts of VOA , China Daily , Global Times , CGTN are retrieved) are used to collect the news that contain “China” AND “poverty reduction” OR “poverty alleviation” OR “poverty elimination” OR “poverty eradication” within the publishing time span from November, 8th, 2012 (on which the 18th National Congress of the CPC set the goal of building a moderately prosperous society in all respects in 2020 and then the country launched an eight-year battle against poverty eradication) to February 25th, 2021 (on which China declared complete victory in eradicating absolute poverty in China). Since the topic of China’s poverty alleviation received much more coverage in Chinese media than American media, there is a great difference in the size of the two corpora. To guarantee the balance between the sizes of the two corpora, the study determines the sum of tokens in Chinese media according to that in American media. The search of American news results in a corpus composed of 62 articles with 60,613 tokens in total. To approach this number, the study selects the top 18 articles in each of the four Chinese newspaper by relevance to the search keywords. The Chinese news corpus is finally built consisting of 72 articles with 60,285 tokens. The detailed representation of basic news data in two comparable corpora is shown in Table 1 . To ensure the accuracy of the results, we only collect news concerning this topic, excluding editorials and commentaries on the event from the corpus. The repetitive and unrelated articles are deleted through manual identification, so is the unnecessary information of time, places, authors’ names and websites. Table 1 The corpus of the Chinese and American news. Corpus News agency Number of articles Sum of articles Tokens CPANC China Daily 18 72 60,285 Xinhua Agency 18 Global Times 18 CGTN 18 APANC The New York Times 20 62 60,613 CNN 15 Bloomberg 15 VOA 12 3.2 Data identification and annotation Based on the semantic parameters identified in studies of evaluative language by Bednarek ( 2008 ) and Martin and White ( 2005 ), and with reference to the way Huan ( 2017 ) identifies appraisal semantic parameters, this study distinguishes between three main appraisal parameters for describing semantic elements of Attitude: the appraised (i.e. the one whose behaviors or characters are evaluated); the appraiser (i.e. the one who evaluates other persons’ or objects’ behaviors or characters); and the attitude itself (i.e. the particular category of Attitude involved). In this study, the identification of the appraised starts with the generation of keyword lists. Taking BNC (British National Corpus) as the reference corpus, this study extracts the respective top 50 noun keywords in CPANC and APANC (Table 3 ) to observe the most frequently appraised. It is found that the top 50 nouns in two lists can be grouped into four categories of appraised, namely: implementors of poverty alleviation, recipients of poverty alleviation, actions taken in poverty alleviation, and outcomes of poverty alleviation. The above four kinds of discourse elements constitute the appraised discourse entities. Based on Xin’s ( 2014 ) categorization of news actors, the appraisers are divided into two categories: author-appraiser and other-appraiser. Author-appraisers refer to the “journalist and media” themselves. Other-appraisers represents the viewpoints of four additional types of news sources—namely, party and government, experts and scholars, social organizations, and the general public. Table 2 shows the categories of appraised and appraisers identified in the present study. Table 2 Categories of appraised and appraisers identified in the corpus. Appraised Appraiser Implementors of poverty alleviation Actions taken in poverty alleviation Recipients of poverty alleviation Outcomes of poverty alleviation author-appraiser other-appraiser Party and government Experts and scholars Social organizations The general public Unspecified The analysis of attitude is conducted through the computer-aided manual annotation of the corpus. The reason of manual annotation is that the accurate identification of appraisal meaning depends on the context, and the automatic computer assistance cannot fully give satisfactory results considering that Appraisal approach is located as an interpersonal system at the level of discourse semantics (Martin & White, 2005 , p.33). Following Bednarek’s ( 2008 , p.152) method, we analyze the appraisal items twice with a sufficiently large time interval of two months between the first and second analyses. When annotating and categorizing all the lexis that carry a positive or negative appraisal value, the guidelines and examples provided in Martin and White ( 2005 ) and the coding choices outlined in other studies that apply the appraisal framework are frequently consulted for help. The specific annotation procedure is described as follows. The corpora collected are converted to plain text format and imported into the UAM Corpus Tool 3.3 (O’Donnell, 2008 ), which offers multiple functions to facilitate manual annotation. An Attitude scheme consisting of “layers’ needs to be imported to the toolkit. Figure 1 represents the different layers of the Attitude coding system used for this study. This study begins with manually annotating the concordances as specific appraiser, appraised, and attitude resource (of certain type, polarity and explicitness) within the Attitude system. When the tagging is finished, annotated items are automatically retrieved through filters to produce statistics and frequency lists for further analysis. All attitude resources of two sub-corpora can be obtained by means of the UAM Corpus Tool 3.3 through manual annotation. When identifying attitude resources, the study follows two principles proposed by Cavasso and Taboada ( 2021 ): minimality and contextuality. Minimality means the shortest unit, or “span” as they refer to it, annotated to show attitudinal information. The length of the unit can vary from a single word to a whole sentence. Contextuality means the consideration of context when identifying the categories that attitudes and polarities belong to. 3.3 Analytical framework and procedure In general, the current study takes qualitative attitudinal analysis combined with quantitative corpus-assisted analysis. A corpus-assisted approach enables the appraisal study (of which only the Attitude system is used here) to capture details of linguistic representation of quantitatively sufficient data and avoid manually laborious work. The qualitative attitudinal analysis helps create an elaborate description, explanation and interpretation on evaluative meanings in the context, minimizing inaccuracy caused by computerized annotation due to the neglect of context. These two approaches are complementarily deployed to compare the ways in which China and its poverty alleviation practice are evaluated. Figure 2 shows the analytical framework for this study. The analytical procedure of this study is demonstrated as follows. First, identify the appraised entities through corpus-assisted approach. We begin by using AntConc 4.2.0 to obtain keyword lists of CPANC and APANC, and extract the respective top 50 keywords (only nouns are considered) to see the most frequently occurring appraised. Then, the top 50 nouns are carefully categorized in two different groups. Second, extract all the sample concordances of the top 50 nouns on the frequency lists of CPANC and APANC and identify all possible attitude resources by considering the context. All the concordances are imported to the UAM Corpus Tool and annotated as specific appraisers, appraised, and attitude resources. Third, when the annotation is complete, annotated items are automatically retrieved through filters to produce statistics and frequency lists for further analysis. Comparisons are then made to see whether there are any significant similarities or differences in frequency of attitude resources within and across categories towards different appraised of China’s poverty alleviation between CPANC and APANC. Lastly, based on the observed results, the attitudinal meanings conveyed by the two media regarding China’s poverty alleviation are unveiled. We aim to make a comprehensive qualitative discussion of social factors that contribute to the similarities and differences. Furthermore, we will offer potential future implications for the Chinese news reporting in the context of international communication concerning poverty alleviation. 4. Distribution of the appraisal parameters 4.1 Category of appraised entities According to Scott and Tribble (2006, p.55), keyword is a textual concept, referring to those lexical items of significance to the text at stake, because of their “unusual(marked)-frequency in comparison with a reference corpus of some suitable kind”. The study takes BNC as the reference corpus, and extracts the respective top 50 keywords in CPANC and APANC due to the list length. Among the keywords, nouns are particularly selected to see the most frequently appraised. Table 3 presents the noun keyword lists of two corpora. Table 3 Top 50 noun keywords of CPANC and APANC. CPANC APANC Rank Keyword (Noun) Keyness Rank Keyword (Noun) Keyness 1 poverty 8,209.18 1 poverty 7,157.03 2 alleviation 6,603.64 2 China 6,713.69 3 China 5,578.92 3 Xi 6,045.67 4 reduction 4,436.77 4 alleviation 5,968.18 5 Xi 4,048.89 5 villagers 5,897.34 6 development 3,610.87 6 government 4,752.45 7 CPC 2,800.56 7 Xinjiang 3,551.93 8 villages 2,599.63 8 Beijing 3,200.96 9 people 1,910.52 9 Jinping 2,991.39 10 Jinping 1,880.16 10 Uyghurs 2,668.66 11 income 1,777.43 11 coronavirus 2,561.63 12 Internet 1,560.96 12 pandemic 1,831.69 13 growth 1,445.24 13 officials 1,584.20 14 villagers 1,351.98 14 farmers 1,492.23 15 efforts 1,333.35 15 authorities 1,464.41 16 infrastructure 1,252.78 16 Tibet 1,356.35 17 county 1,165.94 17 residents 1,349.97 18 residents 1,001.92 18 minorities 1,236.39 19 resources 959.67 19 inequality 1,189.93 20 eradication 909.54 20 Hong 1,082.98 21 cooperation 849.68 21 campaign 1,075.53 22 investment 788.49 22 spending 953.78 23 party 735.30 23 income 923.55 24 relief 727.96 24 party 852.40 25 assistance 667.73 25 people 751.97 26 province 641.01 26 policy 723.67 27 victory 632.56 27 COVID 600.94 28 government 625.55 28 president 591.39 29 prosperity 620.17 29 welfare 568.69 30 farmers 617.51 30 communist 561.65 31 experience 597.46 31 target 557.35 32 achievements 576.68 32 subsidies 556.26 33 president 523.89 33 propaganda 494.67 34 technology 509.56 34 jobs 463.89 35 leadership 490.27 35 migrants 420.56 36 tourism 471.42 36 employment 398.39 37 commerce 465.78 37 growth 375.66 38 goal 423.67 38 efforts 371.64 39 officials 398.38 39 funds 363.55 40 relocation 356.60 40 program 293.82 41 training 327.38 41 countryside 291.38 42 education 307.69 42 province 256.59 43 job 299.42 43 system 194.85 44 growth 289.59 44 goal 179.86 45 policy 297.06 45 development 178.84 46 implementation 291.32 46 village 169.49 47 program 286.81 47 education 154.93 48 plan 273.46 48 loans 136.26 49 system 199.82 49 project 117.07 50 construction 174.88 50 corruption 107.96 As shown in Table 3 , we can discover striking similarities in the semantic category of nouns in both corpora, and thus categorized them as the appraised in the discourse of China’s poverty alleviation. The top 50 nouns in two lists can be grouped into four categories of appraised entities which are indicated in the following Table 4 . Table 4 Categories of appraised in CPANC and APANC. Appraised category Keywords (Noun) in CPANC Keywords (Noun) in APANC Implementors of poverty alleviation China, Xi, CPC, Jinping, Party, government, president, leadership, officials, system China, Xi, government, Beijing, Jinping, officials, authorities, Party, president, communist, system Actions taken in poverty alleviation alleviation, reduction, Internet, efforts, infrastructure, resources, eradication, cooperation, investment, relief, assistance, technology, tourism, commerce, relocation, training, education, job, policy, implementation, program, plan, construction alleviation, campaign, spending, policy, welfare, subsidies, propaganda, employment, jobs, efforts, funds, program, education, loans, project Recipients of poverty alleviation villages, people, villagers, county, residents, province, farmers villagers, Xinjiang, Uyghurs, farmers, Tibet, residents, minorities, Hong, people, migrants, countryside, province, village Outcomes of poverty alleviation development, income, growth, victory, prosperity, experience, achievements, growth inequality, growth, development, corruption Through keyword analysis, the appraised entities in the discourse of China’s poverty alleviation can be identified as the four categories above. All the concordances of the 50 top nouns in keyword lists are then extracted and manually annotated under the framework of attitude system. We have extracted 2,380 concordances from CPANC and 2,022 concordances from APANC. Out of all the extracted concordances, a total of 1,195 attitude tokens have been identified within the two corpora, with 506 tokens found in CPANC and 689 tokens in APANC. These results are presented in Table 5 . Table 5 The overall attitude tokens in sample concordances. Sum of sample concordances Sum of attitude tokens Percentage CPANC 2,380 506 21.3% APANC 2,022 689 34.1% Total 4,402 1,195 27.1% We can notice that in Table 5 the frequency of attitude resources in APANC surpasses that in CPANC, indicating that the American media outlets have a stronger tendency to utilize attitude resources to express their evaluations on China’s poverty alleviation compared to the Chinese ones. After attitude annotation, a statistical overview of the frequency distribution of appraised is then achieved as in the following Table 6 . Table 6 Overall frequency distribution of the appraised entities. Implementors Recipients Actions Outcomes Total CPANC 67 (13.2%) 109 (21.6%) 201 (39.7%) 129 (25.5%) 506 APANC 247 (35.8%) 236 (34.3%) 73 (10.6%) 133 (19.3%) 689 Table 6 indicates a notable disparity in the appraisal priorities of American and Chinese media outlets regarding China’s poverty alleviation. The data demonstrates that Chinese media outlets primarily concentrate on the various “actions” undertaken to alleviate poverty, accounting for 39.7% of their coverage. By evaluating the concrete steps and measures implemented to tackle poverty, media outlets demonstrate the commitment and efforts of the Chinese government and relevant authorities. In contrast, American media places a significant emphasis on the “implementors” and “recipients”, constituting 35.8% and 34.3% of their coverage respectively. The focus on “implementors”, which shows that American media may tend to evaluate Chinese government’s poverty alleviation efforts as part of their broader narrative of China’s political system. Regarding the “recipients”, it can be inferred that it tends to emphasize the impact on individual lives, which is an effective way to generate empathy from the audience. 4.2 Types of appraisers All propositions, according to Hunston ( 2011 , p.34), are either averred (construed as spoken by the author) or attributed (construed as spoken by someone else). The evaluation that is averred by the authorial voice is distinguished from that is attributed to non-authorial voices (Hunston & Sinclair, 2000 ). Selecting the appropriate appraiser in discourse is instrumental in conveying the media’s attitude and stance regarding the presented information. Table 7 reveals significant differences in the distribution of appraisers between CPANC and APANC. Table 7 Overall distribution of appraisers. Appraiser CPANC APANC Author-appraisers 277 (54.7%) 302 (43.8%) Other-appraisers 229 (45.3%) 387 (56.2%) Party and government 98 (19.4%) 64 (9.3%) Experts and scholars 79 (15.6%) 126 (18.3%) Social organizations 28 (5.5%) 43 (6.2%) General public 19 (3.8%) 142 (20.6%) Unspecified 5 (1.0%) 12 (1.8%) Total 506 (100%) 689 (100%) According to Table 7 , CPANC exhibits a greater proportion of appraisal made by author-appraisers, as opposed to APANC. This observation suggests that Chinese media places a stronger reliance on appraisals and viewpoints originating from within the newspaper itself. In contrast, APANC displays a higher percentage of evaluations made by other-appraisers, in comparison to CPANC. The prominence of other-appraisers in American media indicates a willingness to incorporate a broader range of voices and provide a platform for diverse viewpoints. About the subcategories of other-appraisers, the hierarchy of other sources also differs between the two media. In CPANC, the highest-priority other-appraiser is party and government, succeeded by experts and scholars, social organizations, and finally the public. CPANC’s prioritization of party and government sources suggests a potential alignment with official perspectives and political narratives. Audiences might treat this as a reflection of official stances, thus considering the media as a tool for reinforcing government policies, social cohesion, or national identity. Conversely, in APANC, the public takes precedence as the primary other-appraiser, followed by experts and scholars, party and government, and social organizations. APANC’s preference for the public, which is a different orientation compared to CPANC, implies a focus on public sentiment and a desire to represent grassroots perspectives. In addition, Chinese and American media both attach importance to the evaluations made by experts and scholars, constituting 15.6% and 18.3%, respectively. This indicates a common belief in the importance of academic and informed perspectives in shaping public understanding. 4.3 Distribution of attitude resources Table 8 presents the total frequencies of attitude resources in CPANC and APANC. The statistical results highlight a significant difference in the frequency of type, polarity and explicitness of attitude resources between two corpora. Table 8 Overall distribution of attitude resources. CPANC APANC Type Affect 101 (20.0%) 202 (29.3%) Judgment 122 (24.1%) 371 (53.8%) Appreciation 283 (55.9%) 116 (16.9%) Polarity Positive 424 (83.8%) 161 (23.4%) Negative 82 (16.2%) 528 (76.6%) Explicitness Inscribed 355 (70.2%) 269 (39.0%) Evoked 151 (29.8%) 420 (61.0%) Total 506 689 With respect to attitude type, Appreciation is the most frequently employed resource within CPANC, accounting for 55.9% of the frequencies, a figure that even surpasses the combined frequencies of Judgment and Affect. Since Appreciation mainly involves assessment of things and social phenomena, it can be preliminarily inferred that Chinese media primarily makes evaluations on the objects, events, and actions related to the poverty alleviation process. By contrast, Judgment is the most frequently employed resource within APANC, making up 53.8% of the frequencies, followed by Affect and Appreciation. Judgment focuses on evaluating the behavior of individuals based on social sanctions and social esteem. Hence the American media are inclined to use Judgment to assess whether the measures and actions undertaken by the Chinese government are legally and morally appropriate or not. In terms of attitude polarity, a substantial number of positive attitude resources are predominantly utilized within CPANC, amounting to a total of 424 occurrences, representing 83.8% of the total. This indicates that Chinese media generally exhibit a positive stance towards China’s poverty alleviation practices. In contrast, most attitude resources, comprising 76.6% of the total, suggest a negative meaning within APANC. The American media, in general, harbors skepticism towards China’s poverty alleviation practices. As to explicitness, CPANC stands out for its distinctive use of inscribed resources, through which attitudes are mostly straightforward expressed. In contrast, APANC is primarily characterized using evoked resources, through which attitudes are most not explicitly stated or openly given. As a result, the readers are invited to engage in evaluation. By employing this strategy, a degree of caution is exercised to prevent the perception of undue imposition of subjective biases onto the readers. 5. Attitudes on the Appraised entities As discussed in the above section, there exist four common appraised in Chinese and American news reports— “implementors”, “recipients”, “actions”, and “outcomes”. The next four sections then try to further examine the way different appraised are constructed and evaluated in a text. This analysis will offer insights into the media’s attitude and stance on various aspects of China’s poverty alleviation, thus enabling more precise interpretations of social meaning. 5.1 Implementors of poverty alleviation As shown in Table 6 above, American media exhibit much more attitude resources (35.8%) to the appraised of “implementors of poverty alleviation” compared to Chinese media (13.2%). Consistent with their overall dispositions, American media predominantly adopt a negative stance, while Chinese media often take a positive stance according to Table 9 . Both media can be characterized as evoked-dominant. JUDGMENT accounts for the largest of attitude resources in both corpora, suggesting both media try to examine whether the implementors’ behavior live up to ethics and social norm. However, difference lies in that the JUDGMENT appears mainly as “impropriety”, “inveracity”, and “incapacity” in APANC, while in CPANC mainly as “capacity” and “tenacity”. Chinese media outlets tend to utilize the resources of “capacity” and “tenacity” to characterize Chinese implementors as competent, trustworthy, and resilient. In contrast, American media pay the greatest attention to evaluating the “implementors” in their news coverage, and they frequently rely on a significant number of “impropriety”, “inveracity”, and “incapacity” resources to label Chinese implementors as illegitimate, dishonest and incapable. Table 9 Distribution of attitude resources to implementors of poverty alleviation. CPANC APANC AFFECT +inclination +security +satisfaction -security 23 (4.5%) 12 5 4 2 +inclination -inclination -satisfaction -security 41 (5.9%) 31 4 4 2 JUDGMENT +capacity +tenacity +propriety +veracity -tenacity -propriety 44 (8.7%) 18 14 4 3 4 1 -propriety -capacity -veracity -tenacity -normality +capacity +tenacity 206 (29.9%) 67 48 36 15 13 22 5 APPRECIATION — 0 — 0 Total 67 (13.2%) 247 (35.8%) Positive: Negative 60:7 58: 189 Inscribed: Evoked 22:45 86: 161 5.2 Recipients of poverty alleviation Table 10 shows the way the recipients are evaluated in both media. Concerning the appraised of “recipients of poverty alleviation”, the use of attitude resources is notably higher in American media (34.3%) in contrast to Chinese media (21.6%). AFFECT accounts for the largest of attitude resources in both media, indicating both media focus on the emotions of the recipients. The AFFECT appears mainly as “un/happiness”, “in/security” and “dis/satisfaction” in both media, with APANC primarily adopting a negative emotion, while CPANC exhibiting a more balanced approach, incorporating both positive and negative polarities. Another distinction is that CPANC is inscribed-dominant, while APANC evoked-dominant. Chinese media, by using positive realis AFFECT resources, depict the targeted population as experiencing feelings of relief, contentment, and happiness because of poverty alleviation practices. Also, negative attitude resources are uncommonly used to highlight the notable changes recipients experienced, further proving the efficiency of anti-poverty efforts. However, American media give heed to the appraised of “recipients” and employ a large amount of negative AFFECT resources to highlight their negative emotions, thereby depicting them as frustrated, complaint-prone, and distrustful regarding China’s poverty alleviation efforts. A majority of negative evaluations stem from the public, with origins that are either absent or unclear, resulting in lack of authenticity and objectivity. Table 10 Distribution of attitude resources to recipients of poverty alleviation. CPANC APANC AFFECT +happiness +satisfaction +security -security -happiness +inclination 76 (15.1%) 19 15 10 14 12 6 -happiness -security -satisfaction -inclination +inclination +satisfaction 161 (23.4%) 51 46 39 16 5 4 JUDGMENT -capacity -normality +capacity +normality 33 (6.5%) 12 9 6 6 -normality -capacity +capacity 75 (10.9%) 34 29 12 APPRECIATION — 0 — 0 Total 109 (21.6%) 236 (34.3%) Positive: Negative 62:47 21: 215 Inscribed: Evoked 94:15 49: 187 5.3 Actions taken in poverty alleviation As for the actions taken in poverty alleviation, Chinese media (39.7%) exhibit a significantly greater utilization of attitude resources compared to American media (10.6%). Chinese media continue to maintain a positive polarity, whereas American media stay negative. Another notable distinction is that when evaluating “actions”, Chinese media predominantly relies on APPRECIATION as the prevailing resource, whereas their American counterparts primarily employ JUDGMENT, with a minor utilization of APPRECIATION. Both media are inscribed-dominant. The distribution for this kind of evaluation is indicated in Table 11 . Table 11 Distribution of attitude resources to actions taken in poverty alleviation. CPANC APANC AFFECT — 0 — 0 JUDGMENT +capacity +tenacity +propriety -propriety 22 (4.3%) 9 6 4 3 -capacity -propriety -tenacity -veracity +capacity 43 (6.2%) 10 9 9 9 6 APPRECIATION +valuation +reaction +composition -reaction -composition 179 (35.4%) 68 59 31 13 8 -composition -valuation +reaction 30 (4.4%) 15 10 5 Total 201 (39.7%) 73 (10.6%) Positive: Negative 177:24 11: 62 Inscribed: Evoked 142:59 52: 21 Chinese media place the greatest emphasis on evaluating the “actions” in their news coverage and tend to utilize positive APPRECIATION resources to commend a variety of measures for their principles of people-centeredness, innovativeness, comprehensiveness and tailored approaches. Nevertheless, their American counterparts rely heavily on JUDGMENT and slightly on APPRECIATION to underscore the inefficiency, injustice, ethical concerns, superficiality, and unsustainability during the anti-poverty process. 5.4 Outcomes of poverty alleviation The following Table 12 shows the attitude resources used for the outcomes of the poverty alleviation practice. Like the appraised of “actions”, when evaluating “outcomes”, Chinese media primarily relies on positive APPRECIATION as the dominant resource, while American relies on APPRECIATION and minor JUDGMENT. Slightly different, American media utilize a greater number of positive resources as opposed to negative ones. Both media are inscribed-dominant. Chinese media employ many positive APPRECIATION resources towards the “outcomes” to emphasize the significance of poverty alleviation achievements both within local context and on a global level. The dominance of evaluations made by “party and government” contributes to the effective integration of the poverty alleviation narrative into a wider national context. However, American counterparts employ a combination of positive and negative JUDGMENT along with APPRECIATION to express a more nuanced, mixed stance towards the outcomes. This approach involves acknowledging the achieved outcomes while also maintaining a degree of skepticism regarding the underlying political motives. Table 12 Distribution of attitude resources to outcomes of poverty alleviation. CPANC APANC AFFECT — 0 — 0 JUDGMENT +capacity +veracity +tenacity -capacity 25 (4.9%) 11 7 5 2 -capacity +capacity -tenacity +normality -veracity 47 (6.8%) 15 14 9 5 4 APPRECIATION +reaction +valuation +composition -reaction 104 (20.6%) 51 36 15 2 +valuation +reaction -composition -valuation -reaction 86 (12.5%) 38 14 21 9 4 Total 129 (25.5%) 133 (19.3%) Positive: Negative 125: 4 71: 62 Inscribed: Evoked 97: 32 82: 51 6. Attitudinal meanings and their motivations 6.1 Attitudinal meanings foregrounded Drawing from an examination of attitudes in Chinese and American news reports, a discernible contrast emerges in the attitudinal meanings of the two media concerning China’s poverty alleviation. Chinese media highlight the effective leadership, people-centered approach, successful measures, and substantial achievements in reducing poverty. Firstly, Chinese news reports emphasize that poverty alleviation is acknowledged as closely tied to the leadership of the CPC and the effective implementation by various levels of government. Secondly, in Chinese official media, poverty alleviation is guided by the principle of putting people at the core of poverty alleviation efforts, ensuring that the well-being and needs of those receiving assistance are a top priority. Thirdly, Chinese media applaud the effectiveness of the strategies of targeted poverty alleviation, portraying them as well-designed and impactful. Lastly, Chinese media foregrounded that the anti-poverty results are marked by significant improvements in living standards, the eradication of backwardness in poverty-stricken areas, the mindset transformation among those lifted from poverty, and the provision of valuable lessons for other developing nations. American media highlight the underlying motives, efficacy in improving lives, fairness, sustainability, legitimacy, transparency of the policies and measures, and the validity of results. The meaning types foregrounded in American media are listed as follows. Firstly, American reports express doubt that poverty alleviation is primarily used as a tool for consolidating power within the ruling party, rather than a genuine commitment to improving the livelihoods of the poor. Secondly, American media raise doubts about the actual improvement in the lives of the targeted population, suggesting that the living standards of the poor have not substantially improved, leading to dissatisfaction and grievances. Thirdly, American reports highlight various concerns about the specific policies and measures employed for poverty alleviation, including unfair and unscientific poverty standards, the unsustainability of significant fund investments, environmental degradation, corruption undermining legitimacy, and human rights violations. Transparency and authenticity of data are also questioned. Lastly, although American media acknowledge that China has lifted thousands out of poverty to some extent, there’s skepticism about the validity of claims that the goal of eradicating absolute poverty by 2020 was fully achieved as planned. 6.2 Ideological differences For one thing, poverty alleviation has emerged as a nationally endorsed priority, garnering cross-sectoral support from all societal strata in China. On November 3rd, 2013, President Xi Jinping, during his inspection of Shibadong village in Huayuan County, Hunan Province, for the first time introduced the concept of “targeted poverty alleviation”, emphasizing that poverty alleviation should be realistic and tailored to local conditions 3 . Therefore, poverty alleviation, as a national strategy and campaign in China, is positively perceived by the Chinese media as an integral component of the country’s political commitment to achieving common prosperity and the ideal of building a community with a shared future for the whole nation. China’s policy to poverty alleviation is firmly grounded in a people-oriented consensus and driven by the overarching goal of achieving common prosperity. As the governments of all levels grapples with its domestic poverty challenges, the media plays a proactive role and serves as a staunch advocate, a dedicated facilitator, and a significant contributor to the mission of eradicating poverty. Owing to the above fact, the commitment to inform and promote this national strategy constitutes one of the underlying ideologies of the mainstream media. Chinese mainstream media, besides conveying information to the public, function as important tools for the maintenance of social stability in China. It is thus basically ideological, and meanwhile, it constitutes the main site to construct and shape the ideology. The ideology in Chinese news discourse serves as a belief system to convey specific worldviews and perspectives about the socialist society, and a guide to the CPC’s actions in ensuring the legitimization and operationalization of socio-political practices. Since it is ideological in nature, Chinese mainstream media discourse also has a counter-hegemony function. Hegemony serves to convince individuals and social classes to accept the social values and norms of a system of inherent exploitation. Chinese news discourse is intended to counter the hegemony led by the western ruling power and establish an independent discourse system. Furthermore, China has entered a new era after nearly 40 years of development, and a discourse of cultural confidence has been proposed to unify the whole nation and used as an ideological tool for promoting the central government’s new strategic policies. Now the Chinese government is eager to reconstruct a new identity in the world. A lot of new discourses such as “a community of shared future for mankind” and “a responsible major power” have been proposed and thus greatly propelled interaction between China and the rest of the world. Owing to this fact, the main Chinese media is now a very important platform for exhibiting self-confidence. The cultural confidence also influences the interactional model of Chinese mainstream media (Liu, 2023 ). As for the Western press’s preference for criticism, Buck and Liu ( 2010 ) has given a relatively simple but insightful explanation – the western media’s critical approach has its origin in an argumentative and satirical tradition with religious roots while Chinese culture advocates and practices harmony instead of conflict. A famous Chinese scholar Qian Zhongshu once pointed out that western culture is a “duel” culture while Chinese culture is a “duet” culture (This idea was presented by Xu Yuanchong, one of Qian Zhongshu’s students, at a guest lecture for the 85th anniversary of the Foreign Languages and Literatures Department, Tsinghua University, on April 16, 2011.). Nevertheless, too much emphasis on “conflict” and “negative otherization” deepens misunderstanding and thus hampers the communication between China and the rest of the world. Moreover, American media’s critical stance on China’s poverty alleviation can be attributed to a perceived challenge to its national superiority and self-centric political perspective provoked by China’s ascent on the global stage. In mainstream U.S. media, a strong sense of national and ethnic superiority has historically been evident when comparing U.S. practices and values with those of other nations (Xie, 2003 ). After the Cold War, the U.S. established itself as a unipolar power, leading in economic size, international influence, military might, and cultural impact. This self-perceived superiority was attributed to the victory of its system, which led to an excessive inflation of national superiority and self-centeredness. However, in the 21st century, China became explicitly labeled as a strategic competitor. As Zhao ( 2018 ) pointed out, the U.S. is concerned about China’s political and economic reach in developing nations through initiatives like the “Belt and Road Initiative”, as well as its perceived “political infiltration” in democratic countries. These concerns have led to a renewed wave of “demonization” of China, driven by fears of China’s influence extending into both developing and democratic world. 6.3 Cultural values In addition to the contrasting sociopolitical contexts, the divergent historical and cultural values deeply embedded in China and the U.S. are crucial to shaping the distinct attitudinal orientations reflected in their respective media landscapes. The endeavors to alleviate poverty in China are deeply rooted in the rich tapestry of historical and cultural wisdom, with Confucianism being a prominent representative. The significance of “poverty alleviation” extends far beyond mere economic improvement; it serves as a compass for defining the essence of humanity and the ideals of a thriving society (Wang & Zhang, 2020 ). Confucianism espouses the concept of “ Tianxia Datong ”, which pursues a noble cause for a harmonious society attainable by humanity, embodying a utopian vision. At its core, “ Datong ” envisions a society where mutual love and support abound, where every household experience peace and prosperity without the specters of exploitation or oppression. The ideals of “ Tianxia Datong ” are firmly anchored in a profound understanding of human nature and society in China. In this philosophy, the family and the state are intrinsically intertwined, with each individual bearing distinct responsibilities. Consequently, the state should create necessary conditions, including access to education and employment, to enable all individuals to fulfill their obligations. Meanwhile, the state must ensure the well-being of those who find themselves without homes or security. Such a society, characterized by mutual support and shared responsibilities, is undeniably a noble aspiration worth relentless pursuit. The primary objective of poverty alleviation, therefore, transcends mere wealth redistribution; it is a concerted effort to bridge the gap separating the affluent from the underprivileged. Its goal is to nurture a profound sense of happiness and fulfillment among all members of society, while all nurturing the grand ideal of “ Tianxia Datong ”. In contrast to China’s philosophy of collaboration for the benefit of all, the U.S. is deeply entrenched in a value tradition of dualistic polarization marked by a “positive-self versus negative-other” dynamic and the pervasive “anti-communist propaganda filter” (Cheng, 2021 ). Polarization strategy in discourse (van Dijk, 1998 ) revolves around the construction of an “us” identity within a group, sharply contrasted with a “them” identity attributed to those outside the group. Consequently, specific discourse contexts often manifest a clear polarization between “us” and “them”. Said ( 1979 ) emphasizes that the construction of a group’s identity is intricately linked to the simultaneous creation of the opposing “other” identity. In the context of American media, this tendency to construct group identity and foster polarization is evident when discussing topics such as poverty alleviation in China. The discourse surrounding China is consciously framed as the “other” to underscore the presumed cultural superiority of the U.S. This biased discourse reflects a strong inclination towards Western centrism and reinforces the “positive-self versus negative-other” narrative. The “anti-communist propaganda filter”, as a concept, encapsulates the findings of Herman and Chomsky ( 1988 ) in their research on the mechanisms at play in American media reporting. Their work reveals that anti-communism serves as a prominent control mechanism within American news media. Issues are consistently framed in a binary world of communism versus anti-communism. Supporting “our side” is considered entirely legitimate in American news practice. They further contend that anti-communism has become almost akin to a religion within the American media industry, with most individuals fully internalizing its tenets. This internalization means that anti-communism has now become a subconscious, inherent element in how events and news stories are reported. 7. Conclusion The main findings drawn from this study are as follows: 1) Chinese media adopts a positive stance while American media takes a negative perspective prevailingly concerning Chinese poverty alleviation; 2) Chinese media focuses on “actions taken towards poverty alleviation”, while American media focus on “implementors” and “recipients”; 3) in evaluating “implementors”, American media employs “impropriety”, “inveracity”, and “incapacity” resources to portray Chinese implementors as illegitimate, dishonest, and incapable, while Chinese media utilizes “capacity” and “tenacity” resources to characterize them as competent, trustworthy, and resilient. Concerning “recipients”, both media use AFFECT, with American media presenting them as frustrated, complaint-prone, and distrustful, while Chinese media depicts them as content and happy due to the poverty alleviation efforts. On “actions”, Chinese media heavily employs APPRECIATION resources to commend the measures that are characterized by principles of people-centeredness, innovativeness, comprehensiveness, and tailored approaches, while American media relies on more JUDGMENT and minimal APPRECIATION to highlight inefficiency, injustice, superficiality, unsustainability, and ethical concerns in the measures taken. On “outcomes”, Chinese media still employs APPRECIATION resources to praise the results for improving well-being and contributing to the global anti-poverty cause, whereas American media adopt a more nuanced, mixed stance, acknowledging the achieved outcomes while also expressing skepticism about political motives. The attitudinal differences stem from differing political ideologies, sociocultural values, and communication strategies between these two countries. As a linguistically-grounded case study in cross-cultural mediation, this research investigates how poverty eradication narratives function as semiotic constructs within global discourse ecosystems. Media representations of these socioeconomic transformations constitute a constitutive element of contemporary geopolitical discourse, with Chinese and Western outlets articulating divergent narratives rooted in competing epistemological frameworks. This dialectical relationship between media systems consequently shapes the trajectory of Sino-global relations. Persistent discursive disjunctures observed in current communicative practices underscore the necessity for comparative rhetorical analysis. The recognition and identification of convergence and divergence in these transnational narratives will be a significant step for constructing a better world for mankind. Notes 1. Speech at a National Conference to Review the Fight against Poverty and Commend Individuals and Groups Involved. (February 25th, 2021). People’s Daily. Retrieved from http://politics.people.com.cn/n1/2021/0226/c1024-32037098.html 2. Poverty Alleviation: China’s Experience and Contribution. The State Council Information Office of the People’s Republic of China. (April 6th, 2021). Retrieved from https://www.gov.cn/zhengce/2021-04/06/content_5597952.htm 3. Xi Jinping advised the Tujia ethnic group: Work hard and have a bright future. The Central People’s Government of the People’s Republic of China. (November 3rd, 2013). Retrieved from https://www.gov.cn/ldhd/2013-11/04/content_2521045.htm Declarations Fu nding Declaration The authors received no financial support for the research, authorship, and/or publication of this article. Competing Interests The authors declare no competing interests. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethical Statements This article does not contain any studies with human participants performed by any of the authors. References Bednarek M (2008) Emotion Talk across Corpora. Palgrave Macmillan, London Buck M, Liu L (2010) The Argumentative Tradition of Western Media. International Communication , (4) Cavasso L, Taboada M (2021) A Corpus Analysis of Online News Comments Using the Appraisal Framework. J Corpora Discourse Stud 4(1):1–38. https://doi.org/10.18573/JCADS.61 Cheng J (2021) Image of China in U.S. Mainstream Media: A Public Opinion Research on NYT’s China-related Reports. J Intell 11:80–86. https://doi.org/10.3969/j.issn.1002-1965.2021.11.012 Herman E, Chomsky N (1988) Manufacturing Consent: The Political Economy of the Mass Media. Pantheon, New York Huan C (2017) The Strategic Ritual of Emotionality in Chinese and Australian Hard News: A Corpus-based Study. Crit Discourse Stud 14(5):461–479. https://doi.org/10.1080/17405904.2017.1352002 Huang M (2020) Issue Linkage and Linkage Building: The Analysis of the Media Network Agenda of the New York Times Reports on Poverty Alleviation in China (2006–2018). Journalism Communication, (3), 21–36, 126 Hunston S (2011) Corpus Approaches to Evaluation: Phraseology and Evaluative Language. Routledge Hunston S, Sinclair J (2000) A Local Grammar of Evaluation. In: Hunston. S, G.Thompson. (ed) Evaluation in Text: Authorial Stance and the Construction of Discourse. OUP, pp 74–101 Liu L (2023) Communicating with the World. Routledge, London Martin JR, White PRR (2005) The Language of Evaluation. Palgrave Macmillan O’Donnell M (2008) The UAM Corpus Tool: Software for Corpus Annotation and Exploration. In Bretones, C. M., Carmen, M., Ramiro, S. S., (Eds.) Applied Linguistics Now: Understanding Language and Mind (pp.1433–1447). Universidad de Almería Said E (1979) Orientalism. Random House Scott M, Christopher T (2006) Textual Patterns: Keyword and Corpus Analysis in Language Education. John Benjamins Shi A, Wang P (2019) Fractured News Framing: Analyzing the Double-edged Discursive System for China’s ‘Anti-poverty’ and ‘Human Rights’ in the New York Times. Journalism Res, (5), 1–12, 116 van Dijk T (1998) Opinions and Ideologies in the Press. In: Bell A, Garret P (eds) Approaches to Media Discourse. Blackwell Wang H, Zhang Y (2020) Moral Significance and Discourse Value: Poverty Eradication from the Perspective of International Communication. Int Commun, (10), 52–56 Xie Y (2003) Public Channel and Political Product—An Analysis of the Democratic Function of the US Mass Media. Fudan J (Social Sciences) 259–68. https://doi.org/10.3969/j.issn.0257-0289.2003.02.009 Xin B (2014) A Comparative Study of Reported Speech in Chinese and English Newspaper News. Contemporary Rhetoric , (4), 43–50. https://doi.org/CNKI:SUN:XCXX.0.2014-04-005 Yang F, Qiu B (2021) Construction of Poverty Alleviation Issues in China Daily in the Context of Global Governance: An Empirical Analysis Based on Twitter Platform. Mod Communication 362–69. https://doi.org/10.3969/j.issn.1007-8770.2021.03.010 Zhao M (2018) The Trump Doctrine and the Strategic Transformation of U.S.-China Relations. Chin J Am Stud, (5), 26–48 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":54980,"visible":true,"origin":"","legend":"\u003cp\u003eThe coding system of Attitude in this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544117/v1/adcb6152b7c99cbb1dc2fdd1.png"},{"id":97139744,"identity":"fbb7f1b4-0615-4b0e-8ff6-2a3b71b178bb","added_by":"auto","created_at":"2025-12-01 10:02:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18736,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical framework of the present study.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544117/v1/a0891a05291c5b68f3acc884.png"},{"id":108183140,"identity":"cb40c085-521e-43ef-b7af-01a927028d52","added_by":"auto","created_at":"2026-04-30 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Introduction","content":"\u003cp\u003ePoverty is one of the greatest challenges threatening the development of human society. Since the 18th National Congress of the Communist Party of China in 2017 set the goal of building a moderately prosperous society in all respects, China has given high priorities to poverty eradication on the agenda and launched a vigorous battle against poverty. On February, 25th, 2021, a grand gathering ceremony was held to mark China\u0026rsquo;s accomplishments in poverty alleviation and honor model poverty fighters\u003csup\u003e1\u003c/sup\u003e. Through the poverty alleviation project, it is claimed in the official report that China has completely eradicated extreme poverty, making an important contribution to the cause of global poverty alleviation. From 2012 to 2020, 98.99\u0026nbsp;million impoverished rural residents living under the current poverty line were lifted out of poverty. Also, 832 impoverished counties and 128,000 villages were removed from the nation\u0026rsquo;s poverty list during that period. The livelihood of impoverished residents has been dramatically improved in terms of medical care, education, housing and drinking water. The annual amount of disposable income per capita for rural residents in impoverished areas increased from 6,079 yuan in 2013 to 12,588 yuan in 2020.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAs one of the social practices in China, poverty alleviation holds significance not only within its own context but also on a global scale, for it might provide poverty alleviation experience for developing countries. However, the social reality is usually constructed by via discourses, and the same social practice might be represented quite differently in different discourses. It thus seems meaningful to examine the ways in which international media\u0026rsquo;s representation about China and its poverty alleviation. Following this assumption, this study, drawing upon the appraisal approach in systemic functional linguistics (Martin \u0026amp; White, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), intends to compare the ways in which China and its poverty alleviation are evaluated in Chinese and American news coverages. Specifically, this study aims to answer the following questions: (1) What are the distributional features of attitude resources in the Chinese and American news reports? (2) What are the distributional features of attitude resources towards specific appraised entities in the Chinese and American news reports? (3) And what are the underlying motivations that contribute to the differences?\u003c/p\u003e"},{"header":"2. News discourse concerning China’s poverty alleviation","content":"\u003cp\u003eIn news discourse studies, a well-documented body of research explores news discourse related to China\u0026rsquo;s poverty alleviation practices, but these studies predominantly approach the subject from the perspective of international communication instead of the linguistic perspective. In the field of international communication, researches often examine how overseas media construct issues and establish the reporting agenda for China\u0026rsquo;s poverty alleviation efforts. For instance, from the perspective of the agenda-setting framing, Shi and Wang (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) discovered that in \u003cem\u003eThe New York Times\u003c/em\u003e\u0026rsquo; reporting, the topics of China\u0026rsquo;s poverty alleviation and human rights present a \u0026ldquo;fragmented framing\u0026rdquo;; and the role of poverty alleviation in China\u0026rsquo;s human rights progress has been weakened, thus constructing a negative image of China in the discourse of \u0026ldquo;human rights\u0026rdquo;. Similarly, Huang (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that the global significance of China\u0026rsquo;s poverty alleviation has been obscured in the international media, and the political motives behind poverty alleviation are excessively emphasized; external factors such as global trade are highlighted for their role in China\u0026rsquo;s poverty reduction, while the efforts of the Chinese government itself are overlooked. A prevailing view within Chinese academia attributes Western media\u0026rsquo;s imbalanced coverage of China to deeply entrenched ideological predispositions.\u003c/p\u003e\u003cp\u003eIn response to the biases and stereotypes propagated by western media, Chinese scholars have made efforts to offer insights that can contribute to the refinement of China\u0026rsquo;s international discourse model, which might enable China to effectively articulate China\u0026rsquo;s own positive narratives about the poverty alleviation practice. Yang and Qiu (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a study on the topic selection and communication effectiveness of poverty alleviation by \u003cem\u003eChina Daily\u003c/em\u003e on Twitter, and found that the most effective types of topics include those of international poverty alleviation, human development capability, and green development. In terms of the specific narrative approaches, the research indicates that the use of individual narratives and visualizations can bring about positive communication effects, while there is a lack of correlation between data listing and communication effectiveness.\u003c/p\u003e\u003cp\u003eCompared with above studies about narrative model, research by Wang and Zhang (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) focuses on how to eliminate the western media\u0026rsquo;s skepticism towards China\u0026rsquo;s poverty alleviation. They found that western media\u0026rsquo;s skepticism primarily stems from disparities in values, ideologies, and institutional mechanisms between China and the western world. More specifically, this skepticism can be attributed to three main factors: ideological biases, divergent poverty alleviation systems, and challenges in effectively bridging the narrative gap. Addressing these raised concerns, they proposed that it is necessary for China to prioritize intercultural communication and exchange, diminish the ideological undertones in its foreign reports, and enhance the construction of China\u0026rsquo;s international discourse model concerning poverty alleviation.\u003c/p\u003e\u003cp\u003eAs is summarized, however, little attention has been directed towards descriptive research that starts from a discourse perspective to explore the attitude meaning employed in news texts. Firstly, there is a noticeable gap in the exploration of news topics related to China\u0026rsquo;s poverty alleviation from a linguistic perspective, particularly through the application of appraisal approach. Secondly, on the topic of China\u0026rsquo;s poverty alleviation, comparative analyses of Chinese and foreign media coverage remain understudied, yet are essential for examining potential discrepancies between China\u0026rsquo;s intended international narratives and global audiences\u0026rsquo; actual perceptions of its developmental initiatives. Thirdly, the discourse interaction surrounding China\u0026rsquo;s poverty alleviation serves as a paradigmatic case of intercultural communication between Eastern and Western civilizations. Systematic examination of such interactions provides critical insights for bridging ideological divides and fostering deep understanding.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Corpus and data collection\u003c/h2\u003e\u003cp\u003eTwo comparative corpora composed of English news texts regarding China\u0026rsquo;s poverty alleviation respectively from Chinese and American media are compiled. The Chinese corpus, termed as Chinese Poverty Alleviation News Corpus (CPANC), is retrieved from Chinese mainstream media \u0026mdash; \u003cem\u003eChina Daily\u003c/em\u003e, \u003cem\u003eXinhua Agency\u003c/em\u003e, \u003cem\u003eGlobal Times\u003c/em\u003e, and \u003cem\u003eCGTN\u003c/em\u003e. The American corpus, entitled as American Poverty Alleviation News Corpus (APANC), is collected from American mainstream media\u0026mdash; the \u003cem\u003eNew York Times\u003c/em\u003e, \u003cem\u003eCable News Network (CNN)\u003c/em\u003e, \u003cem\u003eBloomberg\u003c/em\u003e, and \u003cem\u003eVoice of America (VOA)\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe online newspaper database \u003cem\u003eLexisNexis\u003c/em\u003e (from which the news texts of \u003cem\u003eNew York Times\u003c/em\u003e, \u003cem\u003eCNN\u003c/em\u003e, \u003cem\u003eBloomberg\u003c/em\u003e, \u003cem\u003eXinhua Agency\u003c/em\u003e are retrieved) along with the websites of newspaper (from which the news texts of \u003cem\u003eVOA\u003c/em\u003e, \u003cem\u003eChina Daily\u003c/em\u003e, \u003cem\u003eGlobal Times\u003c/em\u003e, \u003cem\u003eCGTN\u003c/em\u003e are retrieved) are used to collect the news that contain \u0026ldquo;China\u0026rdquo; AND \u0026ldquo;poverty reduction\u0026rdquo; OR \u0026ldquo;poverty alleviation\u0026rdquo; OR \u0026ldquo;poverty elimination\u0026rdquo; OR \u0026ldquo;poverty eradication\u0026rdquo; within the publishing time span from November, 8th, 2012 (on which the 18th National Congress of the CPC set the goal of building a moderately prosperous society in all respects in 2020 and then the country launched an eight-year battle against poverty eradication) to February 25th, 2021 (on which China declared complete victory in eradicating absolute poverty in China).\u003c/p\u003e\u003cp\u003eSince the topic of China\u0026rsquo;s poverty alleviation received much more coverage in Chinese media than American media, there is a great difference in the size of the two corpora. To guarantee the balance between the sizes of the two corpora, the study determines the sum of tokens in Chinese media according to that in American media. The search of American news results in a corpus composed of 62 articles with 60,613 tokens in total. To approach this number, the study selects the top 18 articles in each of the four Chinese newspaper by relevance to the search keywords. The Chinese news corpus is finally built consisting of 72 articles with 60,285 tokens. The detailed representation of basic news data in two comparable corpora is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTo ensure the accuracy of the results, we only collect news concerning this topic, excluding editorials and commentaries on the event from the corpus. The repetitive and unrelated articles are deleted through manual identification, so is the unnecessary information of time, places, authors\u0026rsquo; names and websites.\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe corpus of the Chinese and American news.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorpus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNews agency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of articles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of articles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTokens\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eChina Daily\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e60,285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eXinhua Agency\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGlobal Times\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCGTN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eThe New York Times\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e60,613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCNN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBloomberg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eVOA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Data identification and annotation\u003c/h2\u003e\u003cp\u003eBased on the semantic parameters identified in studies of evaluative language by Bednarek (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Martin and White (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and with reference to the way Huan (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identifies appraisal semantic parameters, this study distinguishes between three main appraisal parameters for describing semantic elements of Attitude: the appraised (i.e. the one whose behaviors or characters are evaluated); the appraiser (i.e. the one who evaluates other persons\u0026rsquo; or objects\u0026rsquo; behaviors or characters); and the attitude itself (i.e. the particular category of Attitude involved).\u003c/p\u003e\u003cp\u003eIn this study, the identification of the appraised starts with the generation of keyword lists. Taking BNC (British National Corpus) as the reference corpus, this study extracts the respective top 50 noun keywords in CPANC and APANC (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to observe the most frequently appraised. It is found that the top 50 nouns in two lists can be grouped into four categories of appraised, namely: implementors of poverty alleviation, recipients of poverty alleviation, actions taken in poverty alleviation, and outcomes of poverty alleviation. The above four kinds of discourse elements constitute the appraised discourse entities. Based on Xin\u0026rsquo;s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) categorization of news actors, the appraisers are divided into two categories: author-appraiser and other-appraiser. Author-appraisers refer to the \u0026ldquo;journalist and media\u0026rdquo; themselves. Other-appraisers represents the viewpoints of four additional types of news sources\u0026mdash;namely, party and government, experts and scholars, social organizations, and the general public. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the categories of appraised and appraisers identified in the present study.\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCategories of appraised and appraisers identified in the corpus.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAppraised\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAppraiser\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImplementors of poverty alleviation\u003c/p\u003e\u003cp\u003eActions taken in poverty alleviation\u003c/p\u003e\u003cp\u003eRecipients of poverty alleviation\u003c/p\u003e\u003cp\u003eOutcomes of poverty alleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eauthor-appraiser\u003c/p\u003e\u003cp\u003eother-appraiser\u003c/p\u003e\u003cp\u003eParty and government\u003c/p\u003e\u003cp\u003eExperts and scholars\u003c/p\u003e\u003cp\u003eSocial organizations\u003c/p\u003e\u003cp\u003eThe general public\u003c/p\u003e\u003cp\u003eUnspecified\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\u003eThe analysis of attitude is conducted through the computer-aided manual annotation of the corpus. The reason of manual annotation is that the accurate identification of appraisal meaning depends on the context, and the automatic computer assistance cannot fully give satisfactory results considering that Appraisal approach is located as an interpersonal system at the level of discourse semantics (Martin \u0026amp; White, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, p.33).\u003c/p\u003e\u003cp\u003eFollowing Bednarek\u0026rsquo;s (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, p.152) method, we analyze the appraisal items twice with a sufficiently large time interval of two months between the first and second analyses. When annotating and categorizing all the lexis that carry a positive or negative appraisal value, the guidelines and examples provided in Martin and White (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and the coding choices outlined in other studies that apply the appraisal framework are frequently consulted for help.\u003c/p\u003e\u003cp\u003eThe specific annotation procedure is described as follows. The corpora collected are converted to plain text format and imported into the UAM Corpus Tool 3.3 (O\u0026rsquo;Donnell, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which offers multiple functions to facilitate manual annotation. An Attitude scheme consisting of \u0026ldquo;layers\u0026rsquo; needs to be imported to the toolkit. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the different layers of the Attitude coding system used for this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study begins with manually annotating the concordances as specific appraiser, appraised, and attitude resource (of certain type, polarity and explicitness) within the Attitude system. When the tagging is finished, annotated items are automatically retrieved through filters to produce statistics and frequency lists for further analysis.\u003c/p\u003e\u003cp\u003eAll attitude resources of two sub-corpora can be obtained by means of the UAM Corpus Tool 3.3 through manual annotation. When identifying attitude resources, the study follows two principles proposed by Cavasso and Taboada (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e): minimality and contextuality. Minimality means the shortest unit, or \u0026ldquo;span\u0026rdquo; as they refer to it, annotated to show attitudinal information. The length of the unit can vary from a single word to a whole sentence. Contextuality means the consideration of context when identifying the categories that attitudes and polarities belong to.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analytical framework and procedure\u003c/h2\u003e\u003cp\u003eIn general, the current study takes qualitative attitudinal analysis combined with quantitative corpus-assisted analysis. A corpus-assisted approach enables the appraisal study (of which only the Attitude system is used here) to capture details of linguistic representation of quantitatively sufficient data and avoid manually laborious work. The qualitative attitudinal analysis helps create an elaborate description, explanation and interpretation on evaluative meanings in the context, minimizing inaccuracy caused by computerized annotation due to the neglect of context. These two approaches are complementarily deployed to compare the ways in which China and its poverty alleviation practice are evaluated. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the analytical framework for this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analytical procedure of this study is demonstrated as follows.\u003c/p\u003e\u003cp\u003eFirst, identify the appraised entities through corpus-assisted approach. We begin by using AntConc 4.2.0 to obtain keyword lists of CPANC and APANC, and extract the respective top 50 keywords (only nouns are considered) to see the most frequently occurring appraised. Then, the top 50 nouns are carefully categorized in two different groups.\u003c/p\u003e\u003cp\u003eSecond, extract all the sample concordances of the top 50 nouns on the frequency lists of CPANC and APANC and identify all possible attitude resources by considering the context. All the concordances are imported to the UAM Corpus Tool and annotated as specific appraisers, appraised, and attitude resources.\u003c/p\u003e\u003cp\u003eThird, when the annotation is complete, annotated items are automatically retrieved through filters to produce statistics and frequency lists for further analysis. Comparisons are then made to see whether there are any significant similarities or differences in frequency of attitude resources within and across categories towards different appraised of China\u0026rsquo;s poverty alleviation between CPANC and APANC.\u003c/p\u003e\u003cp\u003eLastly, based on the observed results, the attitudinal meanings conveyed by the two media regarding China\u0026rsquo;s poverty alleviation are unveiled. We aim to make a comprehensive qualitative discussion of social factors that contribute to the similarities and differences. Furthermore, we will offer potential future implications for the Chinese news reporting in the context of international communication concerning poverty alleviation.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Distribution of the appraisal parameters","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Category of appraised entities\u003c/h2\u003e\u003cp\u003eAccording to Scott and Tribble (2006, p.55), keyword is a textual concept, referring to those lexical items of significance to the text at stake, because of their \u0026ldquo;unusual(marked)-frequency in comparison with a reference corpus of some suitable kind\u0026rdquo;. The study takes BNC as the reference corpus, and extracts the respective top 50 keywords in CPANC and APANC due to the list length. Among the keywords, nouns are particularly selected to see the most frequently appraised. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the noun keyword lists of two corpora.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop 50 noun keywords of CPANC and APANC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKeyword (Noun)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKeyness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKeyword (Noun)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKeyness\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epoverty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,209.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003epoverty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7,157.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,603.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,713.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,578.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eXi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6,045.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,436.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ealleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5,968.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,048.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003evillagers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5,897.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edevelopment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,610.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003egovernment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4,752.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,800.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eXinjiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3,551.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003evillages\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,599.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeijing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3,200.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epeople\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,910.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJinping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,991.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJinping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,880.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUyghurs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,668.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eincome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,777.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecoronavirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,561.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,560.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003epandemic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,831.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egrowth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,445.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eofficials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,584.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003evillagers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,351.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003efarmers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,492.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eefforts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,333.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eauthorities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,464.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003einfrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,252.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTibet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,356.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecounty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,165.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eresidents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,349.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eresidents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,001.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eminorities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,236.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eresources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e959.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003einequality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,189.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eeradication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e909.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,082.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecooperation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e849.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecampaign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,075.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003einvestment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e788.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003espending\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e953.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eparty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e735.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eincome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e923.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erelief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e727.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eparty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e852.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eassistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e667.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003epeople\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e751.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eprovince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e641.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003epolicy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e723.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003evictory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e632.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCOVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e600.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egovernment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e625.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003epresident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e591.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eprosperity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e620.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ewelfare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e568.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efarmers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e617.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecommunist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e561.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eexperience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e597.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003etarget\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e557.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eachievements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e576.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003esubsidies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e556.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epresident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e523.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003epropaganda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e494.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etechnology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e509.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ejobs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e463.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eleadership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e490.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003emigrants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e420.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etourism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e471.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eemployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e398.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecommerce\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e465.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003egrowth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e375.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egoal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e423.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eefforts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e371.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eofficials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e398.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003efunds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e363.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erelocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e356.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eprogram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e293.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etraining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e327.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecountryside\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e291.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eeducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e307.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eprovince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e256.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ejob\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e299.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003esystem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e194.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egrowth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e289.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003egoal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e179.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epolicy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e297.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edevelopment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e178.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eimplementation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e291.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003evillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e169.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eprogram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e286.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eeducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e154.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eplan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e273.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eloans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e136.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esystem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eproject\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e117.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003econstruction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecorruption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e107.96\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we can discover striking similarities in the semantic category of nouns in both corpora, and thus categorized them as the appraised in the discourse of China\u0026rsquo;s poverty alleviation. The top 50 nouns in two lists can be grouped into four categories of appraised entities which are indicated in the following Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCategories of appraised in CPANC and APANC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAppraised category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKeywords (Noun) in CPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKeywords (Noun) in APANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImplementors of poverty alleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina, Xi, CPC, Jinping, Party, government, president, leadership, officials, system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina, Xi, government, Beijing, Jinping, officials, authorities, Party, president, communist, system\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActions taken in poverty alleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealleviation, reduction, Internet, efforts, infrastructure, resources, eradication, cooperation, investment, relief, assistance, technology, tourism, commerce, relocation, training, education, job, policy, implementation, program, plan, construction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ealleviation, campaign, spending, policy, welfare, subsidies, propaganda, employment, jobs, efforts, funds, program, education, loans, project\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipients of poverty alleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003evillages, people, villagers, county, residents, province, farmers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003evillagers, Xinjiang, Uyghurs, farmers, Tibet, residents, minorities, Hong, people, migrants, countryside, province, village\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcomes of poverty alleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edevelopment, income, growth, victory, prosperity, experience, achievements, growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003einequality, growth, development, corruption\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\u003eThrough keyword analysis, the appraised entities in the discourse of China\u0026rsquo;s poverty alleviation can be identified as the four categories above. All the concordances of the 50 top nouns in keyword lists are then extracted and manually annotated under the framework of attitude system. We have extracted 2,380 concordances from CPANC and 2,022 concordances from APANC. Out of all the extracted concordances, a total of 1,195 attitude tokens have been identified within the two corpora, with 506 tokens found in CPANC and 689 tokens in APANC. These results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\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 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe overall attitude tokens in sample concordances.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of sample concordances\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of attitude tokens\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.1%\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\u003eWe can notice that in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e the frequency of attitude resources in APANC surpasses that in CPANC, indicating that the American media outlets have a stronger tendency to utilize attitude resources to express their evaluations on China\u0026rsquo;s poverty alleviation compared to the Chinese ones. After attitude annotation, a statistical overview of the frequency distribution of appraised is then achieved as in the following Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\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 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall frequency distribution of the appraised entities.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImplementors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecipients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eActions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCPANC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e201 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129 (25.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPANC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e247 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e236 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e133 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e689\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicates a notable disparity in the appraisal priorities of American and Chinese media outlets regarding China\u0026rsquo;s poverty alleviation. The data demonstrates that Chinese media outlets primarily concentrate on the various \u0026ldquo;actions\u0026rdquo; undertaken to alleviate poverty, accounting for 39.7% of their coverage. By evaluating the concrete steps and measures implemented to tackle poverty, media outlets demonstrate the commitment and efforts of the Chinese government and relevant authorities.\u003c/p\u003e\u003cp\u003eIn contrast, American media places a significant emphasis on the \u0026ldquo;implementors\u0026rdquo; and \u0026ldquo;recipients\u0026rdquo;, constituting 35.8% and 34.3% of their coverage respectively. The focus on \u0026ldquo;implementors\u0026rdquo;, which shows that American media may tend to evaluate Chinese government\u0026rsquo;s poverty alleviation efforts as part of their broader narrative of China\u0026rsquo;s political system. Regarding the \u0026ldquo;recipients\u0026rdquo;, it can be inferred that it tends to emphasize the impact on individual lives, which is an effective way to generate empathy from the audience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Types of appraisers\u003c/h2\u003e\u003cp\u003eAll propositions, according to Hunston (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, p.34), are either averred (construed as spoken by the author) or attributed (construed as spoken by someone else). The evaluation that is averred by the authorial voice is distinguished from that is attributed to non-authorial voices (Hunston \u0026amp; Sinclair, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Selecting the appropriate appraiser in discourse is instrumental in conveying the media\u0026rsquo;s attitude and stance regarding the presented information. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e reveals significant differences in the distribution of appraisers between CPANC and APANC.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall distribution of appraisers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAppraiser\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAuthor-appraisers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e277 (54.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e302 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther-appraisers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e229 (45.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387 (56.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty and government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperts and scholars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (18.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial organizations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral public\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e506 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e689 (100%)\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\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, CPANC exhibits a greater proportion of appraisal made by author-appraisers, as opposed to APANC. This observation suggests that Chinese media places a stronger reliance on appraisals and viewpoints originating from within the newspaper itself. In contrast, APANC displays a higher percentage of evaluations made by other-appraisers, in comparison to CPANC. The prominence of other-appraisers in American media indicates a willingness to incorporate a broader range of voices and provide a platform for diverse viewpoints.\u003c/p\u003e\u003cp\u003eAbout the subcategories of other-appraisers, the hierarchy of other sources also differs between the two media. In CPANC, the highest-priority other-appraiser is party and government, succeeded by experts and scholars, social organizations, and finally the public. CPANC\u0026rsquo;s prioritization of party and government sources suggests a potential alignment with official perspectives and political narratives. Audiences might treat this as a reflection of official stances, thus considering the media as a tool for reinforcing government policies, social cohesion, or national identity. Conversely, in APANC, the public takes precedence as the primary other-appraiser, followed by experts and scholars, party and government, and social organizations. APANC\u0026rsquo;s preference for the public, which is a different orientation compared to CPANC, implies a focus on public sentiment and a desire to represent grassroots perspectives. In addition, Chinese and American media both attach importance to the evaluations made by experts and scholars, constituting 15.6% and 18.3%, respectively. This indicates a common belief in the importance of academic and informed perspectives in shaping public understanding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Distribution of attitude resources\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the total frequencies of attitude resources in CPANC and APANC. The statistical results highlight a significant difference in the frequency of type, polarity and explicitness of attitude resources between two corpora.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall distribution of attitude resources.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202 (29.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJudgment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e371 (53.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAppreciation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283 (55.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolarity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e424 (83.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e161 (23.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e528 (76.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExplicitness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInscribed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e355 (70.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420 (61.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e689\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\u003eWith respect to attitude type, Appreciation is the most frequently employed resource within CPANC, accounting for 55.9% of the frequencies, a figure that even surpasses the combined frequencies of Judgment and Affect. Since Appreciation mainly involves assessment of things and social phenomena, it can be preliminarily inferred that Chinese media primarily makes evaluations on the objects, events, and actions related to the poverty alleviation process. By contrast, Judgment is the most frequently employed resource within APANC, making up 53.8% of the frequencies, followed by Affect and Appreciation. Judgment focuses on evaluating the behavior of individuals based on social sanctions and social esteem. Hence the American media are inclined to use Judgment to assess whether the measures and actions undertaken by the Chinese government are legally and morally appropriate or not.\u003c/p\u003e\u003cp\u003eIn terms of attitude polarity, a substantial number of positive attitude resources are predominantly utilized within CPANC, amounting to a total of 424 occurrences, representing 83.8% of the total. This indicates that Chinese media generally exhibit a positive stance towards China\u0026rsquo;s poverty alleviation practices. In contrast, most attitude resources, comprising 76.6% of the total, suggest a negative meaning within APANC. The American media, in general, harbors skepticism towards China\u0026rsquo;s poverty alleviation practices.\u003c/p\u003e\u003cp\u003eAs to explicitness, CPANC stands out for its distinctive use of inscribed resources, through which attitudes are mostly straightforward expressed. In contrast, APANC is primarily characterized using evoked resources, through which attitudes are most not explicitly stated or openly given. As a result, the readers are invited to engage in evaluation. By employing this strategy, a degree of caution is exercised to prevent the perception of undue imposition of subjective biases onto the readers.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Attitudes on the Appraised entities","content":"\u003cp\u003eAs discussed in the above section, there exist four common appraised in Chinese and American news reports\u0026mdash; \u0026ldquo;implementors\u0026rdquo;, \u0026ldquo;recipients\u0026rdquo;, \u0026ldquo;actions\u0026rdquo;, and \u0026ldquo;outcomes\u0026rdquo;. The next four sections then try to further examine the way different appraised are constructed and evaluated in a text. This analysis will offer insights into the media\u0026rsquo;s attitude and stance on various aspects of China\u0026rsquo;s poverty alleviation, thus enabling more precise interpretations of social meaning.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Implementors of poverty alleviation\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e above, American media exhibit much more attitude resources (35.8%) to the appraised of \u0026ldquo;implementors of poverty alleviation\u0026rdquo; compared to Chinese media (13.2%). Consistent with their overall dispositions, American media predominantly adopt a negative stance, while Chinese media often take a positive stance according to Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Both media can be characterized as evoked-dominant. JUDGMENT accounts for the largest of attitude resources in both corpora, suggesting both media try to examine whether the implementors\u0026rsquo; behavior live up to ethics and social norm.\u003c/p\u003e\u003cp\u003eHowever, difference lies in that the JUDGMENT appears mainly as \u0026ldquo;impropriety\u0026rdquo;, \u0026ldquo;inveracity\u0026rdquo;, and \u0026ldquo;incapacity\u0026rdquo; in APANC, while in CPANC mainly as \u0026ldquo;capacity\u0026rdquo; and \u0026ldquo;tenacity\u0026rdquo;. Chinese media outlets tend to utilize the resources of \u0026ldquo;capacity\u0026rdquo; and \u0026ldquo;tenacity\u0026rdquo; to characterize Chinese implementors as competent, trustworthy, and resilient. In contrast, American media pay the greatest attention to evaluating the \u0026ldquo;implementors\u0026rdquo; in their news coverage, and they frequently rely on a significant number of \u0026ldquo;impropriety\u0026rdquo;, \u0026ldquo;inveracity\u0026rdquo;, and \u0026ldquo;incapacity\u0026rdquo; resources to label Chinese implementors as illegitimate, dishonest and incapable.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of attitude resources to implementors of poverty alleviation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAFFECT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+inclination\u003c/p\u003e\u003cp\u003e+security\u003c/p\u003e\u003cp\u003e+satisfaction\u003c/p\u003e\u003cp\u003e-security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (4.5%)\u003c/p\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+inclination\u003c/p\u003e\u003cp\u003e-inclination\u003c/p\u003e\u003cp\u003e-satisfaction\u003c/p\u003e\u003cp\u003e-security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41 (5.9%)\u003c/p\u003e\u003cp\u003e31\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJUDGMENT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003cp\u003e+tenacity\u003c/p\u003e\u003cp\u003e+propriety\u003c/p\u003e\u003cp\u003e+veracity\u003c/p\u003e\u003cp\u003e-tenacity\u003c/p\u003e\u003cp\u003e-propriety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (8.7%)\u003c/p\u003e\u003cp\u003e18\u003c/p\u003e\u003cp\u003e14\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-propriety\u003c/p\u003e\u003cp\u003e-capacity\u003c/p\u003e\u003cp\u003e-veracity\u003c/p\u003e\u003cp\u003e-tenacity\u003c/p\u003e\u003cp\u003e-normality\u003c/p\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003cp\u003e+tenacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e206 (29.9%)\u003c/p\u003e\u003cp\u003e67\u003c/p\u003e\u003cp\u003e48\u003c/p\u003e\u003cp\u003e36\u003c/p\u003e\u003cp\u003e15\u003c/p\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e22\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPPRECIATION\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e247 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive: Negative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e60:7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e58: 189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInscribed: Evoked\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e22:45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e86: 161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Recipients of poverty alleviation\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the way the recipients are evaluated in both media. Concerning the appraised of \u0026ldquo;recipients of poverty alleviation\u0026rdquo;, the use of attitude resources is notably higher in American media (34.3%) in contrast to Chinese media (21.6%). AFFECT accounts for the largest of attitude resources in both media, indicating both media focus on the emotions of the recipients. The AFFECT appears mainly as \u0026ldquo;un/happiness\u0026rdquo;, \u0026ldquo;in/security\u0026rdquo; and \u0026ldquo;dis/satisfaction\u0026rdquo; in both media, with APANC primarily adopting a negative emotion, while CPANC exhibiting a more balanced approach, incorporating both positive and negative polarities. Another distinction is that CPANC is inscribed-dominant, while APANC evoked-dominant.\u003c/p\u003e\u003cp\u003eChinese media, by using positive realis AFFECT resources, depict the targeted population as experiencing feelings of relief, contentment, and happiness because of poverty alleviation practices. Also, negative attitude resources are uncommonly used to highlight the notable changes recipients experienced, further proving the efficiency of anti-poverty efforts. However, American media give heed to the appraised of \u0026ldquo;recipients\u0026rdquo; and employ a large amount of negative AFFECT resources to highlight their negative emotions, thereby depicting them as frustrated, complaint-prone, and distrustful regarding China\u0026rsquo;s poverty alleviation efforts. A majority of negative evaluations stem from the public, with origins that are either absent or unclear, resulting in lack of authenticity and objectivity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of attitude resources to recipients of poverty alleviation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAFFECT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+happiness\u003c/p\u003e\u003cp\u003e+satisfaction\u003c/p\u003e\u003cp\u003e+security\u003c/p\u003e\u003cp\u003e-security\u003c/p\u003e\u003cp\u003e-happiness\u003c/p\u003e\u003cp\u003e+inclination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (15.1%)\u003c/p\u003e\u003cp\u003e19\u003c/p\u003e\u003cp\u003e15\u003c/p\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e14\u003c/p\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-happiness\u003c/p\u003e\u003cp\u003e-security\u003c/p\u003e\u003cp\u003e-satisfaction\u003c/p\u003e\u003cp\u003e-inclination\u003c/p\u003e\u003cp\u003e+inclination\u003c/p\u003e\u003cp\u003e+satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e161 (23.4%)\u003c/p\u003e\u003cp\u003e51\u003c/p\u003e\u003cp\u003e46\u003c/p\u003e\u003cp\u003e39\u003c/p\u003e\u003cp\u003e16\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJUDGMENT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-capacity\u003c/p\u003e \u003cp\u003e-normality\u003c/p\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003cp\u003e+normality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (6.5%)\u003c/p\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-normality\u003c/p\u003e\u003cp\u003e-capacity\u003c/p\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75 (10.9%)\u003c/p\u003e\u003cp\u003e34\u003c/p\u003e\u003cp\u003e29\u003c/p\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPPRECIATION\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e236 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive: Negative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e62:47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21: 215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInscribed: Evoked\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e94:15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e49: 187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Actions taken in poverty alleviation\u003c/h2\u003e\u003cp\u003eAs for the actions taken in poverty alleviation, Chinese media (39.7%) exhibit a significantly greater utilization of attitude resources compared to American media (10.6%). Chinese media continue to maintain a positive polarity, whereas American media stay negative. Another notable distinction is that when evaluating \u0026ldquo;actions\u0026rdquo;, Chinese media predominantly relies on APPRECIATION as the prevailing resource, whereas their American counterparts primarily employ JUDGMENT, with a minor utilization of APPRECIATION. Both media are inscribed-dominant. The distribution for this kind of evaluation is indicated in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of attitude resources to actions taken in poverty alleviation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAFFECT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJUDGMENT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003cp\u003e+tenacity\u003c/p\u003e\u003cp\u003e+propriety\u003c/p\u003e\u003cp\u003e-propriety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (4.3%)\u003c/p\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-capacity\u003c/p\u003e\u003cp\u003e-propriety\u003c/p\u003e\u003cp\u003e-tenacity\u003c/p\u003e\u003cp\u003e-veracity\u003c/p\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43 (6.2%)\u003c/p\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPPRECIATION\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+valuation\u003c/p\u003e\u003cp\u003e+reaction\u003c/p\u003e\u003cp\u003e+composition\u003c/p\u003e\u003cp\u003e-reaction\u003c/p\u003e\u003cp\u003e-composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e179 (35.4%)\u003c/p\u003e\u003cp\u003e68\u003c/p\u003e\u003cp\u003e59\u003c/p\u003e\u003cp\u003e31\u003c/p\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-composition\u003c/p\u003e\u003cp\u003e-valuation\u003c/p\u003e\u003cp\u003e+reaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30 (4.4%)\u003c/p\u003e\u003cp\u003e15\u003c/p\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive: Negative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e177:24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e11: 62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInscribed: Evoked\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e142:59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e52: 21\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\u003eChinese media place the greatest emphasis on evaluating the \u0026ldquo;actions\u0026rdquo; in their news coverage and tend to utilize positive APPRECIATION resources to commend a variety of measures for their principles of people-centeredness, innovativeness, comprehensiveness and tailored approaches. Nevertheless, their American counterparts rely heavily on JUDGMENT and slightly on APPRECIATION to underscore the inefficiency, injustice, ethical concerns, superficiality, and unsustainability during the anti-poverty process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Outcomes of poverty alleviation\u003c/h2\u003e\u003cp\u003eThe following Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows the attitude resources used for the outcomes of the poverty alleviation practice. Like the appraised of \u0026ldquo;actions\u0026rdquo;, when evaluating \u0026ldquo;outcomes\u0026rdquo;, Chinese media primarily relies on positive APPRECIATION as the dominant resource, while American relies on APPRECIATION and minor JUDGMENT. Slightly different, American media utilize a greater number of positive resources as opposed to negative ones. Both media are inscribed-dominant.\u003c/p\u003e\u003cp\u003eChinese media employ many positive APPRECIATION resources towards the \u0026ldquo;outcomes\u0026rdquo; to emphasize the significance of poverty alleviation achievements both within local context and on a global level. The dominance of evaluations made by \u0026ldquo;party and government\u0026rdquo; contributes to the effective integration of the poverty alleviation narrative into a wider national context. However, American counterparts employ a combination of positive and negative JUDGMENT along with APPRECIATION to express a more nuanced, mixed stance towards the outcomes. This approach involves acknowledging the achieved outcomes while also maintaining a degree of skepticism regarding the underlying political motives.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of attitude resources to outcomes of poverty alleviation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCPANC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAPANC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAFFECT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJUDGMENT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003cp\u003e+veracity\u003c/p\u003e\u003cp\u003e+tenacity\u003c/p\u003e\u003cp\u003e-capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (4.9%)\u003c/p\u003e\u003cp\u003e11\u003c/p\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-capacity\u003c/p\u003e\u003cp\u003e+capacity\u003c/p\u003e\u003cp\u003e-tenacity\u003c/p\u003e\u003cp\u003e+normality\u003c/p\u003e\u003cp\u003e-veracity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47 (6.8%)\u003c/p\u003e\u003cp\u003e15\u003c/p\u003e\u003cp\u003e14\u003c/p\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPPRECIATION\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+reaction\u003c/p\u003e\u003cp\u003e+valuation\u003c/p\u003e\u003cp\u003e+composition\u003c/p\u003e\u003cp\u003e-reaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (20.6%)\u003c/p\u003e\u003cp\u003e51\u003c/p\u003e\u003cp\u003e36\u003c/p\u003e\u003cp\u003e15\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+valuation\u003c/p\u003e\u003cp\u003e+reaction\u003c/p\u003e\u003cp\u003e-composition\u003c/p\u003e\u003cp\u003e-valuation\u003c/p\u003e\u003cp\u003e-reaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86 (12.5%)\u003c/p\u003e\u003cp\u003e38\u003c/p\u003e\u003cp\u003e14\u003c/p\u003e\u003cp\u003e21\u003c/p\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129 (25.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e133 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive: Negative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e125: 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e71: 62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInscribed: Evoked\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e97: 32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e82: 51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Attitudinal meanings and their motivations","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Attitudinal meanings foregrounded\u003c/h2\u003e\u003cp\u003eDrawing from an examination of attitudes in Chinese and American news reports, a discernible contrast emerges in the attitudinal meanings of the two media concerning China\u0026rsquo;s poverty alleviation.\u003c/p\u003e\u003cp\u003eChinese media highlight the effective leadership, people-centered approach, successful measures, and substantial achievements in reducing poverty. Firstly, Chinese news reports emphasize that poverty alleviation is acknowledged as closely tied to the leadership of the CPC and the effective implementation by various levels of government. Secondly, in Chinese official media, poverty alleviation is guided by the principle of putting people at the core of poverty alleviation efforts, ensuring that the well-being and needs of those receiving assistance are a top priority. Thirdly, Chinese media applaud the effectiveness of the strategies of targeted poverty alleviation, portraying them as well-designed and impactful. Lastly, Chinese media foregrounded that the anti-poverty results are marked by significant improvements in living standards, the eradication of backwardness in poverty-stricken areas, the mindset transformation among those lifted from poverty, and the provision of valuable lessons for other developing nations.\u003c/p\u003e\u003cp\u003eAmerican media highlight the underlying motives, efficacy in improving lives, fairness, sustainability, legitimacy, transparency of the policies and measures, and the validity of results. The meaning types foregrounded in American media are listed as follows. Firstly, American reports express doubt that poverty alleviation is primarily used as a tool for consolidating power within the ruling party, rather than a genuine commitment to improving the livelihoods of the poor. Secondly, American media raise doubts about the actual improvement in the lives of the targeted population, suggesting that the living standards of the poor have not substantially improved, leading to dissatisfaction and grievances. Thirdly, American reports highlight various concerns about the specific policies and measures employed for poverty alleviation, including unfair and unscientific poverty standards, the unsustainability of significant fund investments, environmental degradation, corruption undermining legitimacy, and human rights violations. Transparency and authenticity of data are also questioned. Lastly, although American media acknowledge that China has lifted thousands out of poverty to some extent, there\u0026rsquo;s skepticism about the validity of claims that the goal of eradicating absolute poverty by 2020 was fully achieved as planned.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Ideological differences\u003c/h2\u003e\u003cp\u003eFor one thing, poverty alleviation has emerged as a nationally endorsed priority, garnering cross-sectoral support from all societal strata in China. On November 3rd, 2013, President Xi Jinping, during his inspection of Shibadong village in Huayuan County, Hunan Province, for the first time introduced the concept of \u0026ldquo;targeted poverty alleviation\u0026rdquo;, emphasizing that poverty alleviation should be realistic and tailored to local conditions\u003csup\u003e3\u003c/sup\u003e. Therefore, poverty alleviation, as a national strategy and campaign in China, is positively perceived by the Chinese media as an integral component of the country\u0026rsquo;s political commitment to achieving common prosperity and the ideal of building a community with a shared future for the whole nation. China\u0026rsquo;s policy to poverty alleviation is firmly grounded in a people-oriented consensus and driven by the overarching goal of achieving common prosperity. As the governments of all levels grapples with its domestic poverty challenges, the media plays a proactive role and serves as a staunch advocate, a dedicated facilitator, and a significant contributor to the mission of eradicating poverty. Owing to the above fact, the commitment to inform and promote this national strategy constitutes one of the underlying ideologies of the mainstream media.\u003c/p\u003e\u003cp\u003eChinese mainstream media, besides conveying information to the public, function as important tools for the maintenance of social stability in China. It is thus basically ideological, and meanwhile, it constitutes the main site to construct and shape the ideology. The ideology in Chinese news discourse serves as a belief system to convey specific worldviews and perspectives about the socialist society, and a guide to the CPC\u0026rsquo;s actions in ensuring the legitimization and operationalization of socio-political practices. Since it is ideological in nature, Chinese mainstream media discourse also has a counter-hegemony function. Hegemony serves to convince individuals and social classes to accept the social values and norms of a system of inherent exploitation. Chinese news discourse is intended to counter the hegemony led by the western ruling power and establish an independent discourse system.\u003c/p\u003e\u003cp\u003eFurthermore, China has entered a new era after nearly 40 years of development, and a discourse of cultural confidence has been proposed to unify the whole nation and used as an ideological tool for promoting the central government\u0026rsquo;s new strategic policies. Now the Chinese government is eager to reconstruct a new identity in the world. A lot of new discourses such as \u0026ldquo;a community of shared future for mankind\u0026rdquo; and \u0026ldquo;a responsible major power\u0026rdquo; have been proposed and thus greatly propelled interaction between China and the rest of the world. Owing to this fact, the main Chinese media is now a very important platform for exhibiting self-confidence. The cultural confidence also influences the interactional model of Chinese mainstream media (Liu, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs for the Western press\u0026rsquo;s preference for criticism, Buck and Liu (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) has given a relatively simple but insightful explanation \u0026ndash; the western media\u0026rsquo;s critical approach has its origin in an argumentative and satirical tradition with religious roots while Chinese culture advocates and practices harmony instead of conflict. A famous Chinese scholar Qian Zhongshu once pointed out that western culture is a \u0026ldquo;duel\u0026rdquo; culture while Chinese culture is a \u0026ldquo;duet\u0026rdquo; culture (This idea was presented by Xu Yuanchong, one of Qian Zhongshu\u0026rsquo;s students, at a guest lecture for the 85th anniversary of the Foreign Languages and Literatures Department, Tsinghua University, on April 16, 2011.). Nevertheless, too much emphasis on \u0026ldquo;conflict\u0026rdquo; and \u0026ldquo;negative otherization\u0026rdquo; deepens misunderstanding and thus hampers the communication between China and the rest of the world.\u003c/p\u003e\u003cp\u003eMoreover, American media\u0026rsquo;s critical stance on China\u0026rsquo;s poverty alleviation can be attributed to a perceived challenge to its national superiority and self-centric political perspective provoked by China\u0026rsquo;s ascent on the global stage. In mainstream U.S. media, a strong sense of national and ethnic superiority has historically been evident when comparing U.S. practices and values with those of other nations (Xie, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). After the Cold War, the U.S. established itself as a unipolar power, leading in economic size, international influence, military might, and cultural impact. This self-perceived superiority was attributed to the victory of its system, which led to an excessive inflation of national superiority and self-centeredness. However, in the 21st century, China became explicitly labeled as a strategic competitor. As Zhao (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) pointed out, the U.S. is concerned about China\u0026rsquo;s political and economic reach in developing nations through initiatives like the \u0026ldquo;Belt and Road Initiative\u0026rdquo;, as well as its perceived \u0026ldquo;political infiltration\u0026rdquo; in democratic countries. These concerns have led to a renewed wave of \u0026ldquo;demonization\u0026rdquo; of China, driven by fears of China\u0026rsquo;s influence extending into both developing and democratic world.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Cultural values\u003c/h2\u003e\u003cp\u003eIn addition to the contrasting sociopolitical contexts, the divergent historical and cultural values deeply embedded in China and the U.S. are crucial to shaping the distinct attitudinal orientations reflected in their respective media landscapes.\u003c/p\u003e\u003cp\u003eThe endeavors to alleviate poverty in China are deeply rooted in the rich tapestry of historical and cultural wisdom, with Confucianism being a prominent representative. The significance of \u0026ldquo;poverty alleviation\u0026rdquo; extends far beyond mere economic improvement; it serves as a compass for defining the essence of humanity and the ideals of a thriving society (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Confucianism espouses the concept of \u0026ldquo;\u003cem\u003eTianxia Datong\u003c/em\u003e\u0026rdquo;, which pursues a noble cause for a harmonious society attainable by humanity, embodying a utopian vision. At its core, \u0026ldquo;\u003cem\u003eDatong\u003c/em\u003e\u0026rdquo; envisions a society where mutual love and support abound, where every household experience peace and prosperity without the specters of exploitation or oppression. The ideals of \u0026ldquo;\u003cem\u003eTianxia Datong\u003c/em\u003e\u0026rdquo; are firmly anchored in a profound understanding of human nature and society in China. In this philosophy, the family and the state are intrinsically intertwined, with each individual bearing distinct responsibilities. Consequently, the state should create necessary conditions, including access to education and employment, to enable all individuals to fulfill their obligations. Meanwhile, the state must ensure the well-being of those who find themselves without homes or security. Such a society, characterized by mutual support and shared responsibilities, is undeniably a noble aspiration worth relentless pursuit. The primary objective of poverty alleviation, therefore, transcends mere wealth redistribution; it is a concerted effort to bridge the gap separating the affluent from the underprivileged. Its goal is to nurture a profound sense of happiness and fulfillment among all members of society, while all nurturing the grand ideal of \u0026ldquo;\u003cem\u003eTianxia Datong\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\u003cp\u003eIn contrast to China\u0026rsquo;s philosophy of collaboration for the benefit of all, the U.S. is deeply entrenched in a value tradition of dualistic polarization marked by a \u0026ldquo;positive-self versus negative-other\u0026rdquo; dynamic and the pervasive \u0026ldquo;anti-communist propaganda filter\u0026rdquo; (Cheng, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Polarization strategy in discourse (van Dijk, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) revolves around the construction of an \u0026ldquo;us\u0026rdquo; identity within a group, sharply contrasted with a \u0026ldquo;them\u0026rdquo; identity attributed to those outside the group. Consequently, specific discourse contexts often manifest a clear polarization between \u0026ldquo;us\u0026rdquo; and \u0026ldquo;them\u0026rdquo;. Said (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) emphasizes that the construction of a group\u0026rsquo;s identity is intricately linked to the simultaneous creation of the opposing \u0026ldquo;other\u0026rdquo; identity. In the context of American media, this tendency to construct group identity and foster polarization is evident when discussing topics such as poverty alleviation in China. The discourse surrounding China is consciously framed as the \u0026ldquo;other\u0026rdquo; to underscore the presumed cultural superiority of the U.S. This biased discourse reflects a strong inclination towards Western centrism and reinforces the \u0026ldquo;positive-self versus negative-other\u0026rdquo; narrative.\u003c/p\u003e\u003cp\u003eThe \u0026ldquo;anti-communist propaganda filter\u0026rdquo;, as a concept, encapsulates the findings of Herman and Chomsky (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) in their research on the mechanisms at play in American media reporting. Their work reveals that anti-communism serves as a prominent control mechanism within American news media. Issues are consistently framed in a binary world of communism versus anti-communism. Supporting \u0026ldquo;our side\u0026rdquo; is considered entirely legitimate in American news practice. They further contend that anti-communism has become almost akin to a religion within the American media industry, with most individuals fully internalizing its tenets. This internalization means that anti-communism has now become a subconscious, inherent element in how events and news stories are reported.\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThe main findings drawn from this study are as follows: 1) Chinese media adopts a positive stance while American media takes a negative perspective prevailingly concerning Chinese poverty alleviation; 2) Chinese media focuses on “actions taken towards poverty alleviation”, while American media focus on “implementors” and “recipients”; 3) in evaluating “implementors”, American media employs “impropriety”, “inveracity”, and “incapacity” resources to portray Chinese implementors as illegitimate, dishonest, and incapable, while Chinese media utilizes “capacity” and “tenacity” resources to characterize them as competent, trustworthy, and resilient. Concerning “recipients”, both media use AFFECT, with American media presenting them as frustrated, complaint-prone, and distrustful, while Chinese media depicts them as content and happy due to the poverty alleviation efforts. On “actions”, Chinese media heavily employs APPRECIATION resources to commend the measures that are characterized by principles of people-centeredness, innovativeness, comprehensiveness, and tailored approaches, while American media relies on more JUDGMENT and minimal APPRECIATION to highlight inefficiency, injustice, superficiality, unsustainability, and ethical concerns in the measures taken. On “outcomes”, Chinese media still employs APPRECIATION resources to praise the results for improving well-being and contributing to the global anti-poverty cause, whereas American media adopt a more nuanced, mixed stance, acknowledging the achieved outcomes while also expressing skepticism about political motives. The attitudinal differences stem from differing political ideologies, sociocultural values, and communication strategies between these two countries.\u003c/p\u003e\u003cp\u003eAs a linguistically-grounded case study in cross-cultural mediation, this research investigates how poverty eradication narratives function as semiotic constructs within global discourse ecosystems. Media representations of these socioeconomic transformations constitute a constitutive element of contemporary geopolitical discourse, with Chinese and Western outlets articulating divergent narratives rooted in competing epistemological frameworks. This dialectical relationship between media systems consequently shapes the trajectory of Sino-global relations. Persistent discursive disjunctures observed in current communicative practices underscore the necessity for comparative rhetorical analysis. The recognition and identification of convergence and divergence in these transnational narratives will be a significant step for constructing a better world for mankind.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Notes","content":"\u003cp\u003e\u003cspan\u003e1. Speech at a National Conference to Review the Fight against Poverty and Commend Individuals and Groups Involved. (February 25th, 2021). People\u0026rsquo;s Daily. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://politics.people.com.cn/n1/2021/0226/c1024-32037098.html\u003c/span\u003e\u003c/span\u003e\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2. Poverty Alleviation: China\u0026rsquo;s Experience and Contribution. The State Council Information Office of the People\u0026rsquo;s Republic of China. (April 6th, 2021). Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/zhengce/2021-04/06/content_5597952.htm\u003c/span\u003e\u003c/span\u003e\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e3. Xi Jinping advised the Tujia ethnic group: Work hard and have a bright future. The Central People\u0026rsquo;s Government of the People\u0026rsquo;s Republic of China. (November 3rd, 2013). Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/ldhd/2013-11/04/content_2521045.htm\u003c/span\u003e\u003c/span\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFu\u003c/strong\u003e\u003cstrong\u003ending Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthical Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBednarek M (2008) Emotion Talk across Corpora. 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Chin J Am Stud, (5), 26\u0026ndash;48\u003c/span\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":"Appraisal approach, attitude, news discourse, Chinese media, American media, poverty alleviation","lastPublishedDoi":"10.21203/rs.3.rs-7544117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7544117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to describe and explain the attitude distribution in news discourses about China\u0026rsquo;s poverty alleviation practice in Chinese and American mainstream media with the framework of appraisal approach. The analysis reveals that Chinese media adopts a positive stance primarily employing Appreciation resources, while American media takes a negative perspective relying on Judgement resources. The different distribution of attitudinal meaning is manifested in the evaluation on the four categories of poverty alleviation, that is, implementors, actions, recipients, and outcomes, with Chinese media focusing on actions and American media on implementors and recipients. The attitudinal differences can be traced back to differing political ideologies, sociocultural values, and communication strategies between these two countries. This research contributes to offering insights into how media discourse constructs and reflects sociopolitical realities, influencing international perceptions and intercultural communication.\u003c/p\u003e","manuscriptTitle":"Attitude of Chinese and American media on China’s poverty alleviation based on Appraisal approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 11:03:53","doi":"10.21203/rs.3.rs-7544117/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":"5d73f263-87f8-4137-9210-f9e51ee04e16","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58576483,"name":"Humanities/Cultural and media studies"},{"id":58576484,"name":"Social science/Cultural and media studies"},{"id":58576485,"name":"Humanities/Language and linguistics"},{"id":58576486,"name":"Social science/Language and linguistics"},{"id":58576487,"name":"Social science/Politics and international relations"}],"tags":[],"updatedAt":"2026-04-30T05:25:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 11:03:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7544117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7544117","identity":"rs-7544117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}