From being seen to being coded: Technological practices and intergenerational interactions of older short video creators

From being seen to being coded: Technological practices and intergenerational interactions of older short video creators | 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 From being seen to being coded: Technological practices and intergenerational interactions of older short video creators Peng-Peng Li, Xi Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7135542/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract This study investigates the intersectional dynamics of age, gender, and algorithmic power within the short video production practices of older adults on China's Douyin platform and its affiliated editing tool, CapCut. Using a mixed-methods approach, the research conceptualizes these platforms as a techno-field to explore how older users navigate digital content creation. Findings reveal a pronounced gendered division in technical engagement: older male users tend to adopt advanced editing features to reinforce domestic authority, while older female users predominantly rely on emotionally themed templates to maintain intergenerational bonds. The Douyin algorithm subtly deprioritizes content tagged as "older adults," with female creators disproportionately affected by reduced visibility. Furthermore, algorithms reconfigure intergenerational negotiations, adult children assert authority through technological metrics, while older parents adopt conciliatory strategies to preserve familial intimacy. By illuminating the complex entanglement of digital aging, technogender, and algorithmic governance, this study contributes to the critical understanding of digital media inequalities and offers policy recommendations for enhancing platform transparency and designing age-inclusive tools in the context of global ageing. Physical sciences/Mathematics and computing Social science/Science technology and society short video production ageing intersectionality platform algorithms intergenerational negotiation 1. Introduction Amidst the accelerating global trend of population ageing, China is confronting a structural challenge of “growing old before getting rich” (Tian, 2022). By the end of 2024, the population aged 60 and above in China had reached 313 million, accounting for 22% of the total population, a figure that continues to rise (Jin, 2025). Consequently, “active ageing” has become a central strategic issue at the national level. The Chinese government’s initiative to “build an age-friendly digital society” aims to bridge the digital divide for older adults through technological empowerment (Gao, 2024). However, existing empirical studies have largely framed older adults as passive or disadvantaged users of technology, often overlooking their creative agency in digital cultural production. According to recent data, individuals aged 50 and above constitute 34.1% of China's internet user base, with over 70% engaging with short video platforms (CINIC, 2024). Platforms such as Douyin, Kuaishou (Kwai), and WeChat Channels have created new avenues for older adults to express themselves. Influential accounts like “Kangkang and Grandpa,” “Fashion Granny Squad,” and “Grandma Wang” have garnered millions of followers by sharing daily life, talents, and stories, thereby challenging stereotypes that associate ageing with digital marginality. More importantly, these practices extend beyond mere technological use; they are deeply intertwined with emotional expression, intergenerational bonding, and the reconfiguration of self-worth. CapCut, the core video-editing tool developed by ByteDance and integrated into Douyin, has played a pivotal role in this shift. Since its release in 2019, CapCut has evolved into a powerful yet accessible mobile and desktop editing suite. It integrates non-linear editing functions (e.g., multi-track timelines, speed modulation), AI-assisted features (e.g., automatic subtitles, template matching), and creative modules (e.g., dynamic stickers, multilingual font libraries, voice modulation). With 371 million cumulative downloads and over 800 million monthly active users by Q3 2024, CapCut has driven the global expansion of ByteDance’s ecosystem, with international versions contributing over 60% of total revenue (Zhang, 2023). Its low technical threshold through tools such as “auto-generate clips,” “text-to-video,” AI voice separation, and smart editing has enabled older users to transition from passive consumers to active content producers, giving rise to a new wave of “silver digital culture.” This shift exemplifies how technology can empower individual storytelling and foster intergenerational reciprocity. Younger family members often help older relatives navigate digital tools, while older users employ short videos to document familial events, such as grandchildren’s milestones or holiday gatherings, thereby reinforcing emotional ties (Bucher, 2018). However, tensions persist between the optimistic narrative of technological empowerment and the lived experiences of older users. Most short video platforms are algorithmically optimized for younger demographics, potentially embedding ageist biases into content moderation and recommendation systems. Studies have shown that content tagged with “elderly” or “silver” is more likely to be downranked in algorithmic visibility, especially when produced by older women (Sinanan & Horst, 2021). Simultaneously, the principle of technological neutrality remains a myth, which means design processes are embedded with gendered assumptions extending from user profiling and marketing strategies to interface design. (Song & Xing, 2024; Yang, 2024). Wajcman (2006), for instance, argues that domestic technology use reflects household power structures, with men typically dominating decision-making and women relegated to maintenance tasks. This gendered encoding of technology is also evident in commercial platforms. MacLeod and McArthur (2019), through critical interface analysis, demonstrate how dating apps like Tinder and Bumble reinforce binary gender norms through data fields (e.g., emphasizing female appearance), interaction rules (e.g., pseudo-empowerment of women messaging first), and algorithmic matching systems. Such studies reveal that seemingly “objective” technological standards can function as masked carriers of gendered power, thus reproducing hegemonic norms under the guise of neutrality. In this light, technological design not only reproduces the social construct of "technological masculinity" but also transforms digital literacy into a gendered resource of domestic power. Within family settings, this results in subtle yet persistent forms of power negotiation. Given the growing participation of older adults in short video content creation, this study explores whether gender differences influence how adult children perceive and evaluate their parents' digital outputs, interpreting these evaluations as potential sites of implicit intergenerational negotiation. Existing studies in China on older adults’ engagement with digital platforms remain limited. Some research has examined how usability and family support facilitate technological adoption among the elderly (Cao & Wang, 2024; Xu & Zeng, 2025), yet these often underemphasize the creative and strategic use of technology. Others highlight how older influencers resist age-related stigma through digital labor (Wang & Ming, 2024), but tend to treat older adults as a homogeneous group, lacking intersectional insights into gendered experiences. While studies on the social construction of technology have illuminated the co-constitution of tools and gender identity (Oudshoorn, 2003), they remain inadequate in addressing the dynamic interplay of ageing, technology, and intergenerational power in the current digital ecosystem. Moreover, conceptual models of “digital reciprocity” are often reduced to unidirectional flows from tech guidance to emotional feedback, without accounting for the evolving dynamics of family power or gendered asymmetries. The compounding effects of algorithmic ageism and gender bias, which can reinforce gender scripts through the interface design of both “tech-driven” and “emotion-driven” editing apps, remain overlooked in existing analytical frameworks. As short videos increasingly mediate cross-generational and cross-class communication, reshaping the lived experience of ageing in a media-saturated society, this study raises three central research questions: 1) Do older male and female users demonstrate differentiated patterns in their use of short video production tools? 2) Do digital tools serve as mediators for negotiating power in intergenerational relationships? 3) How do algorithmic mechanisms intersect with age and gender to shape the experiences and outcomes of older users’ digital participation? To answer these questions, this study integrates intersectionality theory with technogender frameworks to uncover both the structural constraints and agentive strategies in older adults’ digital practices. Empirically, it focuses on students enrolled in short video production courses at the University for Retired Cadres in Hangzhou, Zhejiang Province. The following sections offer a theoretical review of affordances, gendered technological construction, domestication theory, and intergenerational reciprocity. The methodology outlines the research setting, data collection, and analytical procedures. The subsequent sections present findings and discussions, followed by theoretical contributions, policy implications, and suggestions for future research. This study ultimately seeks to offer new insights into age-inclusive tool design, intergenerational communication, and algorithmic governance in the context of digital ageing. 2. Literature review 2.1 Technological affordances and the structural embedding of gendered practices The concept of 'affordance,' first introduced by ecological psychologist James Gibson ( 1979 ), refers to the perceived and actual possibilities for action that an environment offers to an organism. Affordances highlight the inseparable relationship between the actor and the environment, serving as a foundational idea for understanding how technologies shape social behavior. When adopted into the sociology of technology, Hutchby ( 2001 ) extended the concept to include media and digital tools, arguing that while technologies enable certain actions, such as social media platforms allowing users to share content, they simultaneously constrain user behavior through embedded limitations, including character counts or template restrictions. In this duality of enablement and restriction, Markus and Silver (2008) further observed that affordances are often guided by subtle visual and symbolic cues, such as button size, color, or textual guidance, which shape user interaction and implicitly convey the designer’s expectations. Hence, technology design is not a neutral “toolbox” but an active mediator in structuring user behavior and social norms. Interfaces may appear to offer freedom of use while in reality embedding prescribed behavioral scripts within the user experience. When examining the intersection of technology and gender, scholars have revealed how design assumptions can reinforce prevailing gender norms. For example, Bivens ( 2017 ) showed that Facebook’s initial gender settings, which only included "male" or "female," marginalized non-binary users. Even after introducing over 50 gender identity options in 2015, the platform continued to algorithmically classify non-binary individuals into residual categories such as “others,” reinforcing a binary framework in ad targeting and user segmentation. Similarly, Schwartz and Neff ( 2019 ), in their study of Craigslist, demonstrated how interface design, such as pink header bars and default prompts like “seeking gentlemen,” replicated traditional gender hierarchies by positioning women as passive and men as active. Technological systems, therefore, act not only as instruments of functionality but also as enforcers of gender norms. Wajcman ( 2006 ) and Oudshoorn ( 2003 ) both emphasized that technology design and development have historically been male-dominated, resulting in tools and systems that reflect male-centric assumptions. For instance, early office technologies and computing systems were designed with male professionals in mind, while even technologies like contraceptives often reduced women’s reproductive health needs to a matter of birth control, illustrating a form of technological disciplining of the female body. In the context of new media, Pan and Liu ( 2017 ) proposed a tripartite model of affordances: information production affordance, social affordance, and mobility affordance. These dimensions reflect how affordances in digital platforms are not static but dynamically embedded in design choices, algorithmic configurations, and interactive rules, all of which participate in reproducing cultural norms and structural inequalities, including those based on gender. Thus, from ecological psychology to media interface studies, the affordance framework reveals how technology, while offering possibilities for action, also reinforces patterns of identity and power. Grounded in this theoretical lineage, this study explores how older adults’ engagement with short video production tools is shaped by gendered affordances and investigates whether such tools reinforce or subvert normative gender expectations. RQ1 Do older male and female users demonstrate differentiated patterns in their use of short video production tools? 2.2 Domestication Theory and Intergenerational Negotiation of Technology While technological affordances may appear neutral, they are often shaped by cultural assumptions and social power relations. The relationship between users and technologies is not one-directional; rather, it involves dynamic negotiation. Domestication theory, introduced by Silverstone and Hirsch ( 1994 ), conceptualizes how users integrate technologies into everyday life through appropriation, objectification, incorporation, and conversion, thereby assigning personal and cultural meanings to technologies. This framework shifts attention from design to use, emphasizing user agency and contextual adaptations. Building on this, Lie and Sørensen ( 1996 ) argued that domestication is a bidirectional process. Although users operate within the functional constraints of a given technology, they can also repurpose it in creative ways, thereby reprogramming its intended use. Haddon ( 2003 ) documented how youth often “hack” or modify software to personalize functions beyond design intent, demonstrating that scripts embedded in technology are not fixed, but modifiable. Similarly, Hamraie ( 2017 ), in disability studies, noted how unintended uses, such as using voice assistants for daily life support, reveal the emancipatory potential of reappropriating technologies, especially for marginalized users. This logic extends to intergenerational media practices. Early studies of the digital divide (e.g., Van Dijk, 2006 ) portrayed older adults as “technologically deficient,” in contrast to younger “digital natives.” However, this linear perspective has been increasingly challenged. For instance, Wang and Wu ( 2022 ) showed that digital guidance within families is rarely unidirectional. While children may offer technical instruction with a sense of superiority, older parents often draw on moral authority to assert influence in familial decision-making. Similarly, Livingstone and Helsper ( 2007 ) observed that youth providing digital support often assert autonomy, transforming technical interactions into tools of intergenerational power negotiation. These insights highlight the limitations of portraying older adults as passive or homogenous. Hargittai and Shafer ( 2006 ) found that although gender differences exist in self-reported digital skills, older users exhibit high levels of adaptability and strategic use in practice, indicating the presence of “digital wisdom” that is often overlooked in mainstream accounts. In the Chinese context, such intergenerational interactions are often embedded in Confucian values of filial piety. Wang and Li ( 2022 ) described the phenomenon of “digital reciprocity,” wherein younger family members provide tech support out of ethical obligations, yet use this process to assert epistemic authority in the household. The dynamic is thus fraught with both emotional labor and power asymmetries. Drawing on Foucault’s ( 1977 ) notion of micro-power, even seemingly benign acts, such as hands-on instruction, can serve as disciplinary mechanisms that subtly shape older adults’ behaviors and communication styles. Here, digital tools become intermediaries for everyday power negotiations. From domestication to reappropriation to intergenerational negotiation, these studies underscore that technologies are embedded in complex sociocultural structures. Within familial contexts, technology use becomes a site where emotions, authority, and care ethics intersect. RQ2 Do digital tools serve as mediators for negotiating power in intergenerational relationships? 2.3 Algorithmic Discipline and the Intersecting Exclusions of Age and Gender To unpack the entanglement of identity and technological systems, this study draws on Crenshaw’s ( 1989 ) theory of intersectionality. Initially applied to highlight how Black women experience oppression differently from both white women and Black men, intersectionality reveals how multiple systems of inequality intersect to produce unique forms of marginalization. This lens is particularly relevant in the digital age, where algorithmic systems encode and amplify structural biases. Gullette ( 2019 ) argued that digital media and intelligent technologies frequently embed ageist assumptions, casting older users as cognitively or physically incapable. This is especially true for older women, whose content is often algorithmically deprioritized due to the double burden of age and gender. Noble’s ( 2018 ) seminal work, Algorithms of Oppression, exposed how seemingly neutral search and recommendation systems reproduce racial and gender hierarchies through historical data patterns. The personalization and automation of such systems cloak their biases in a veil of legitimacy. Bucher ( 2018 ) emphasized the normativity of algorithmic logic, where “if–then” models favor content that is emotionally intense, visually stimulating, and highly interactive. This bias systematically disadvantages certain content creators, including many older users, whose work may be less aggressive or technically polished. Zuboff’s ( 2019 ) concept of “surveillance capitalism” further critiques how even acts of refusal, such as opting out of personalization, are harvested as behavioral data, thereby reinforcing algorithmic control. As a result, older adults’ sporadic interaction patterns are often flagged as “low-value,” which marginalizes them within mainstream digital visibility. Fuchs ( 2014 ) critiqued digital labor economies, arguing that users’ unpaid content production serves as a resource for platform monetization. For older creators, this means that their contributions are algorithmically harvested without corresponding rewards, reinforcing their instrumentalization within platform capitalism. Taken together, algorithmic design constitutes a space of intersecting structural exclusions. First, affordance structures reproduce gendered interactional norms. Second, algorithmic content evaluation reinforces ageist mechanisms of visibility. For older female creators, short video expression becomes both a site of adaptation and a field of resistance. This study, therefore, asks: RQ3 How do algorithmic mechanisms intersect with age and gender to shape the experiences and outcomes of older users’ digital participation? 3. Research design 3.1 Research field and participants This study focuses on a short video production course offered by the Hangzhou University for Retired Cadres, with particular attention to students’ use of CapCut, a short-form video editing tool under the Douyin (TikTok China) ecosystem. Participant selection criteria included: (1) consistent use of CapCut for at least three months, and (2) an average output of no fewer than two short videos per month. Additionally, video content had to center around family life or exhibit clear gendered features (e.g., male users favoring technical editing functions, female users emphasizing emotional storytelling). After providing detailed explanations of research objectives and securing informed consent, 30 core participants (15 male, 15 female) were selected. All participants voluntarily took part in this study. Prior to participation, they were fully informed of the research objectives, procedures, and data usage. Written informed consent was obtained from each participant, and they were assured of their right to withdraw from the study at any time without any consequences. All data were anonymized to protect personal privacy. This field site and participant group offer several notable advantages for exploring domestication processes: (1) The course delivered systematic training in short video production using CapCut from February to July 2024. Participants completed at least 10 videos covering diverse themes—family documentation (e.g., grandchildren’s milestones), skill tutorials (e.g., calligraphy, dance, knitting), and local culture (e.g., Hangzhou landscapes)—offering rich empirical material for analyzing the dynamic process of technological domestication; (2) The participant pool includes 72 men (48%) and 78 women (52%), aged between 53 and 65, with educational backgrounds ranging from middle school to postgraduate level. Their professional experiences span teaching, engineering, medicine, and public service, reflecting the heterogeneity typical in aging and media research; and (3) Approximately 85% of participants actively engage with children and grandchildren via family WeChat groups and Douyin accounts. These communication channels, such as comment sections, private messages, and offline discussions, provide a natural setting to observe dynamics of digital reverse mentoring and intergenerational power negotiation (Sun, 2024). 3.2 Methodology and operational steps Drawing on an intersectional theoretical framework, this study adopts a multi-method approach to investigate the interplay between age, gender, technological design, and cultural practice. Specifically, the research integrates: (1) Technological affordance analysis through interface walkthroughs; (2) User experience interpretation through in-depth interviews; (3) Semiotic and behavioral verification through short video content analysis. Given that CapCut is deeply embedded within the Douyin platform, the interface design of the editing tool cannot be separated from broader algorithmic and content distribution logics, thereby forming a hybrid production environment characterized by tool–platform entanglement. CapCut interface walkthrough Building on the walkthrough methodology proposed by Light et al. (2018), this study explores the interaction chain spanning interface design, user behavior, and cultural scripts within CapCut. Research shows that gendered practices are often subtly encoded in interface affordances, functionality structures, and algorithmic nudges (Sinanan & Horst, 2021). The walkthrough followed three operational steps: (1) Function mapping: Identify gendered functional zones, such as the “tech-focused” module (e.g., keyframes, masking, multi-track editing, chroma key) and the “emotion-oriented” module (e.g., family templates, retro filters, animated typography). (2) Script decoding: Analyze age- and gender-coded cues in interface language (e.g., “One-click auto-edit” signaling efficiency bias; “Capture heartwarming moments” emphasizing emotional labor) and assess how these scripts guide behavior. (3) Simulated user pathing: Create senior user profiles and document the entire “registration–creation–publishing–interaction” workflow to detect design biases (e.g., default templates privileging youth aesthetics) and examine how these interact with Douyin’s algorithmic recommendations.. As shown in Table 1, a structured framework guided data collection and analysis: Table 1 Walkthrough framework for CapCut interface Analytical Dimension Operational Definition Data Collection Method Functional Zone Mapping Categorize CapCut modules (e.g., technical/emotional), record feature names, icons, and hierarchy Interface screenshots, user pathway logs, feature usage statistics Gendered Design Clues Analyze interface copy (e.g., “One-click edit”) and symbolic elements (e.g., color, avatar style) Textual analysis, visual semiotic coding Age Inclusivity Evaluation Assess senior-friendly design (e.g., font size, instructional prompts) and usability thresholds Accessibility testing, learning cost scoring Algorithmic Logic Simulation Use controlled experiments (e.g., tech vs. emotional styles) to infer recommendation biases Monitoring engagement metrics, reverse-engineering platform logic In-Depth Interviews with Older Users To challenge the homogeneous depiction of older users in digital studies, this study conducted in-depth interviews to explore how gender and age shape the use of CapCut and Douyin for video production. Interviews were conducted with the 30 selected participants (15 men, 15 women) between February and October 2024, with informed consent and audio recording permissions obtained. The interview protocol was structured around four dimensions (see Table 2): Table 2 Four-dimensional mixed-method framework Dimension Operational Definition Data Sources Technological Domestication Gendered feature preferences and learning barriers Interviews, walkthrough logs Intergenerational Negotiation Feedback types (technical/emotional) and household division of labor Interviews, family chat logs Algorithmic Response Adaptive strategies to platform rules (e.g., hashtag manipulation) Content analysis, platform data Local Cultural Scripts Influence of filial piety and face-saving norms on tech critique Interviews, field notes Each dimension informed the design of interview questions. Examples include: (1) Domestication: “Why do you prefer certain features (e.g., keyframes)? Do you think some functions are gender-biased?”and“What were your biggest challenges in learning CapCut?”(2) Intergenerational Negotiation:“How do your children respond to your videos? Has their feedback influenced your editing decisions?”and“Does the division of labor in video production reflect traditional gender roles?”(3) Algorithmic Response:“What do you do when your videos don’t get views? Have you changed your hashtags or style?” and “Are you aware of age-related algorithmic suppression?”(4) Cultural Scripts: “How does filial piety affect your willingness to discuss platform biases with your children?” A constructivist thematic analysis was conducted, enhanced by a theory-layered coding scheme: (1) Open Coding: Extracted behavioral tags (e.g., “tag optimization,” “tech assistance frequency”) without imposing theoretical labels. (2) Axial Coding: Clustered tags into the four analytic dimensions (e.g.,“child tech guidance”under intergenerational negotiation). (3) Selective Coding: Modeled interrelations (e.g., “domestication–algorithmic response” reveals how tool mastery drives platform adaptation). (4) Theory Layer: Interpreted dynamics through domestication theory, intersectionality, and Confucian ethics, forming a localized analytic paradigm. Short Video Content Analysis To validate users’ editing practices and expression styles, the study conducted a content analysis of 150 family-themed Douyin videos, randomly sampled from the 30 participant accounts (75 videos by men, 75 by women). Guided by Vera et al. (2024), videos were coded across three key dimensions: technical complexity, emotional expression, and intergenerational interaction. Coding categories included: (1) Technical Complexity: Number of special effects (e.g., split-screen, stickers), transition types (e.g., dissolve, slide), and originality of subtitles (scored 0–3). (2) Emotional Expression: Duration of sentimental background music, presence of intimate shots (e.g., hugs, close-ups), and nostalgic elements (e.g., black-and-white filters, old photos). (3) Intergenerational Interaction: Sentiment polarity in children’s comments (positive/neutral/negative), presence of tech-help requests, and emotional feedback intensity (based on emoji and exclamatory usage). See Table 3. Additional metrics such as completion rate, average watch time, and engagement (likes, comments, shares) were recorded. To ensure inter-rater reliability, two coders underwent 15 hours of training and conducted a pilot test ( n = 20). Final Cohen's Kappa reached 0.82, indicating strong coding consistency. Table 3 Coding framework for Douyin video content Analytical Dimension Specific Indicator Operational Definition Example Technical Complexity Special Effects Count of effects per video (e.g., split-screen, stickers) 1 split-screen + 3 stickers = 4 Transitions Type and count of transitions 2 dissolve transitions = dissolve × 2 Subtitle Originality 0 = no subtitles; 1 = default; 2 = modified; 3 = fully original Personalized poetry = score 3 Emotional Expression Sentimental Music Duration ratio of sentimental music 80s/90s = 88.9% Intimate Shots Number of warm interactions 3 hugging close-ups = 3 Nostalgic Symbols Frequency of nostalgic elements (0–2 scale) B&W filter + 2 old photos = 2 Intergenerational Interaction Comment Sentiment Coded as positive/neutral/negative “Mom, this moved me” = positive Tech-Help Requests Count of guidance-seeking comments “How to do this transition?” Emotional Feedback Based on emoji/exclamation marks: 0 = none; 1 = 1–2; 2 = 3+ Comment “heart and crying” emojis = 2 4. Analysis and Findings 1) Gendered Technological Practices: Differentiation of Elderly “Digital Gestures” on the CapCut Platform The analysis begins by examining how elderly men and women navigated CapCut and its integration with Douyin, focusing on the gendered dynamics shaping their editing practices, feature choices, and creative approaches. Although all participants were trained under an identical curriculum and completed the same video production tasks, their engagement with the platform’s tools and affordances displayed distinct patterns. These differences mirror broader scholarly discussions of gendered relationships with technology, particularly within caregiving and educational settings (Lunt & Livingstone, 1992 ; Silverstone & Hirsch, 1994 ; Lie & Sørensen, 1996 ; Haddon, 2003 ). Our quantitative analysis reveals significant contrasts in tool usage and stylistic orientation between genders. Among the 15 male participants, 13 regularly employed advanced editing features—such as multi-track editing, keyframe manipulation, transition effects, and precise background music adjustment—producing an average of 4.2 high-level effects per video (SD = 1.3). In comparison, the female participants averaged 1.8 high-level effects per video (SD = 0.7), a difference that was statistically significant (t = 6.34, p < 0.01). Marked disparities were also observed in the adoption of specific techniques: split-screen (χ² = 18.7, p < 0.001), masking (χ² = 24.3, p < 0.001), and keyframes (χ² = 31.6, p < 0.001). For instance, Participant M09 (62, retired engineer) combined masking and keyframe functions to create a “grandson growth comparison” video, layering photographs with fade-ins and synchronized background music. He commented, “It must be like an engineering drawing, aligned layer by layer, or it won’t look professional.” This video received a complexity score of 8.7/10, far exceeding the female participants’ mean score of 4.2 (χ² = 34.6, p < 0.001). These patterns reflect a gendered orientation in which male participants approached video editing as an exercise in precision and technical control, consistent with prior observations of “instrumentality” in technology appropriation (Silverstone & Hirsch, 1994 ; Haddon, 2003 ). By contrast, female participants gravitated toward features prioritising emotional resonance and aesthetic effects. Twelve of the 15 women reported frequent reliance on pre-set templates, filters, beautification tools, animated captions, and automatically matched music. Their content tended to emphasise family moments, intimate memories, and everyday crafts, creating a “warm atmosphere” through soft lighting and narrative text. F04 (58, retired teacher) explained, “I make these videos for my granddaughter; it has to be gentle, since emotion is the most important.” These practices illustrate the ways in which women deploy digital tools to sustain relational and identity-based meanings, reinforcing the insights of Silverstone and Hirsch ( 1994 ) and Lie and Sørensen ( 1996 ) on the symbolic dimensions of domesticating media technologies. Learning strategies also displayed gendered patterns. Male participants more often employed exploratory “trial–debug–optimise” methods, repeatedly testing feature combinations to achieve a polished effect. Female participants favoured platform-recommended styles and quick templates, seeking efficiency while preserving emotional expression. This divergence suggests that elderly users’ creative gestures are shaped not only by interface design and algorithmic recommendation logics but also by intersecting gender identities and social expectations (Crenshaw, 1989 ; Bucher, 2018 ). These observed differences may be partially attributed to participants’ professional backgrounds. Many male respondents had careers in computing, engineering, or management, which facilitated the transfer of technical expertise to short video production. Female participants, however, often came from education, caregiving, or service industries, with comparatively limited prior exposure to digital tools. This uneven “technological habitus,” as described by Wajcman ( 2006 ), creates selective affinities for certain features while reinforcing algorithmically mediated user stratification. Aspirations for creative production also differed: four male respondents expressed intentions to monetise or formalise their video-making skills by teaching in senior universities, documenting community events, or running personal channels, whereas female respondents mainly framed video production as a means to share emotions and maintain family bonds, motivated by sentiments such as “letting the children see,” “giving the family a keepsake,” or “recording life fragments.” This confirms that elderly creators actively reinterpret and reconstruct gender roles through their digital practices (Haraway, 2013 ). However, the study also observed notable cases of boundary-crossing and collaborative learning. F02, for example, mastered advanced voice-over and text animation techniques after intensive practice, subsequently sharing these skills with peers. M07, with guidance from his daughter, experimented with cartoon stickers and soft background music, admitting that while initially hesitant, he came to enjoy these “playful” features. These examples illustrate that gendered distinctions are not rigid binaries but dynamic, negotiated practices shaped by family collaboration, peer influence, and algorithmic nudging. 2) Intergenerational Negotiation under Algorithmic Influence: The Gendered Power Structure in Family Feedback To address the second research question, we also explored how elderly users’ short video practices on Douyin intersect with family dynamics and algorithmic processes, shaping intergenerational relations and power structures. Based on a triangulated dataset comprising 30 family WeChat group conversations, 450 sentiment-coded comments from younger family members, and more than 40 in-depth interviews, the analysis traces the evolving connections between Douyin’s recommendation mechanisms, familial discursive patterns, and gendered roles. The findings reveal a pronounced gender asymmetry in intergenerational feedback and symbolic recognition. Among male respondents, 89% (N = 13) frequently received technical inquiries from their children after sharing videos, with requests for editing guidance constituting 37% of all comments. In contrast, only 5% of feedback on women’s works involved technical questions, while emotional responses accounted for as much as 68%. For example, M07, a 60-year-old retired teacher, became known among his family for mastering dynamic tracking and split-screen editing to document his grandson’s piano practice sessions. Comments on his daughter’s WeChat Moments included: “My dad’s skills could teach a class,” alongside inquiries such as “What software did you use?” and “How did you make the transition effects?” Similarly, M09 (62, retired engineer) reflected: “After learning to edit videos on Douyin, my kids think I’ve kept up with the times; now I’m the ‘director’ of our family trips.” These narratives suggest that Douyin’s algorithmically amplified visibility transforms technical expertise into a form of symbolic capital (Bourdieu, 2001 ), strengthening elderly men’s authority and recognition in family hierarchies. The data also indicate that Douyin’s traffic distribution system further magnifies this symbolic value. Technical videos produced by male participants were more likely to enter recommendation streams, reinforcing a cycle of algorithmic endorsement and familial recognition. For instance, M11 (64, retired photographer) created a multi-camera highlight reel of his grandson’s football match, which received substantial algorithmic promotion, prompting his children to remark, “Dad, your video is trending!” Such feedback links algorithmic metrics—such as views, likes, and recommendations—with social validation, positioning technological skills as a key currency in intergenerational negotiations. Women’s creative outputs were primarily evaluated within the framework of emotional labor and private domains. Seventy-six percent of female participants (N = 11) received predominantly sentimental feedback, with comments like “The video is so warm” or “It made me cry,” whereas technical questions were rare. F08’s New Year’s Eve dinner video, for example, was praised in a private message by her daughter—“Mom, you filmed it so warmly”—but elicited no discussion of captioning or music editing. Female participants also reported their videos circulated mostly within closed WeChat family groups or private Douyin shares, often facing algorithmic throttling due to content classification as “emotional.” View counts of female-authored videos were approximately one-fifth of comparable male “technical” content, reflecting how Douyin’s implicit classification and recommendation logics systematically privilege technical aesthetics over emotional expression (Noble, 2018 ). F06 (61, retired bank executive) captured this tension: “Making videos felt like doing homework; my son said I was too sentimental, but when I added effects, he said it looked unnatural.” These findings demonstrate that algorithmic and familial systems intersect to reproduce traditional gender divisions, framing men’s technological practices as forms of public competence and rational authority, while women’s outputs are consistently categorized within the realm of domestic caregiving. This dynamic echoes Bourdieu’s ( 2001 ) theory of symbolic capital conversion, wherein algorithmically endorsed technical expertise is transformed into authority within family structures. It further supports Noble’s ( 2018 ) critique that platform “neutrality” often masks the systemic reproduction of inequality. A further layer of complexity emerges in how Douyin’s design contributes to what can be described as “algorithmic filial ethics.” Traditional Confucian cultural norms emphasize respect for elders and deference to parental authority; however, Douyin’s youthful aesthetic preferences and data-driven ranking mechanisms often invert this hierarchy, positioning younger family members as technical gatekeepers. For example, F10 (58, retired accountant) attempted to experiment with AI-generated messages about her future self but was dismissed by her son as “unrealistic,” prompting her to revert to default templates. Such moments highlight how algorithmic affordances and intergenerational knowledge disparities jointly regulate elderly creators’ technical practices, imposing dual pressures of maintaining family “face” and algorithmic performance metrics. In this sense, children’s evaluations of videos—whether framed as emotional praise or technical critique—become a measurable form of filial piety, quantified and mediated by platform algorithms. Overall, intergenerational feedback on Douyin constitutes more than casual family commentary; it functions as a discursive arena in which gendered power structures are negotiated and reinforced. Men leverage technological expertise to enhance symbolic authority, while women’s creative contributions, though deeply valued emotionally, are constrained to private spheres and deprioritized algorithmically. This reinforces a multi-layered structure of gendered labor, where women assume invisible emotional maintenance roles, and men consolidate leadership through technological performance. In line with Noble’s ( 2018 ) argument that algorithmic infrastructures codify existing social hierarchies, Douyin’s recommendation system emerges as an active mediator of family politics, shaping intergenerational power negotiations in subtle yet persistent ways. Elderly short video creation, therefore, is not only a form of digital expression but also a critical site for examining how algorithmic systems and traditional cultural expectations co-produce symbolic authority, emotional labor, and identity reproduction in later life. 3) Structural Dilemmas of Intersectional Empowerment: The Intertwined Effects of Gender and Age in Implicit Algorithmic Logic Building on this qualitative foundation, the analysis draws on 50 comparative interface walkthroughs and statistical data from 150 short videos to demonstrate that Douyin’s content recommendation framework operates as a classification system in Bowker and Star’s ( 1999 ) sense, systematically translating social hierarchies into automated decisions. Videos containing age-related markers such as “elderly” or “retired” achieved an average exposure rate of only 14 percent compared with similar content without such identifiers (p < 0.01). This disparity is exemplified by participant F05’s calligraphy class video, which received only one-fifth the views of comparable videos lacking age labels. These patterns reveal that algorithmic downgrading effectively codes age as a negative attribute, marginalizing elderly creators within Douyin’s data ecology and reinforcing their peripheral status as users. Gender further amplifies this exclusion, as technical videos by male participants dominate Douyin’s recommendation pool and achieve exposure rates 3.2 times higher than those of female creators (p < 0.05). For example, M11’s football highlight videos, edited with rapid transitions and multiple camera angles, were promoted widely, whereas F08’s similarly themed videos were classified as “emotional content,” limiting their circulation to private networks. This pattern indicates that Douyin’s algorithm privileges markers of technical sophistication that align with traditionally masculine forms of cultural capital (Bourdieu, 2001 ; Wajcman, 2006 ), thereby reinforcing gender hierarchies in creative labor. Visual aesthetics also serve as a disciplinary tool. CapCut’s template library features predominantly youthful imagery, pushing older creators, especially women, to rely on beauty filters and retouching tools to meet audience expectations, often triggering intergenerational critique. F08 remarked, “I need to smooth my skin, but not too much or my kids will dislike it,” illustrating the tension between authenticity, algorithmic favorability, and family approval. Such mechanisms illustrate that classification systems extend beyond content evaluation, shaping creators’ visual self-presentation and identity work. Although structural disadvantages are deeply embedded in Douyin’s algorithmic design, elderly users actively negotiate and resist these constraints through diverse strategies. Some creators enhance the technical complexity of their videos to appeal to the recommendation algorithm, as illustrated by F05’s innovative collage of archival photos with AI-generated captions, which achieved high technical complexity scores but remained confined to a private traffic pool due to algorithmic prioritization of identity markers over production quality. Others develop collective approaches, forming informal online and offline networks to share expertise and co-create tools such as regional dialect voiceovers and enlarged subtitles designed to challenge youth-centric norms. These grassroots initiatives provide technical support and represent symbolic resistance, offering alternative forms of expression. A smaller group of participants has begun advocating for algorithmic transparency, calling for disclosure of recommendation standards, the inclusion of age-inclusivity measures, and the establishment of dedicated “silver-haired traffic pools.” Although these demands have yet to elicit institutional responses, they signal an emerging critical consciousness of algorithmic power and a shift toward collective action (Noble, 2018 ). Nevertheless, the platform’s commercial imperatives severely limit the effectiveness of these resistive practices, ultimately imposing emotional strain and identity fragmentation on older creators. Among female participants, 65% reported adopting age-disguising techniques, often describing a felt alienation from their authentic selves. As F02 reflected, “I always have to think about how to look younger before uploading videos, feeling like I’m not showing my true self.” Even male participants, though generally benefiting from algorithmic promotion, expressed anxiety around sustaining production quality. M12 remarked, “Editing videos is more exhausting than working before retirement,” highlighting the often-invisible affective labor embedded in digital content creation (Fox, 2017 ). These experiences demonstrate that empowerment on the platform remains deeply intertwined with surveillance and self-discipline. Each attempt to circumvent algorithmic marginalization—whether through aesthetic filters, keyword optimization, or technical innovations—paradoxically generates further behavioral data, thereby reinforcing the platform’s classificatory logic and deepening its mechanisms of control. 5. Discussion This study examined elderly users’ short video practices on Douyin and its editing platform CapCut, adopting an intersectionality perspective to reveal the intertwined effects of age, gender, and algorithmic governance. By integrating content analysis, platform walkthroughs, and in-depth interviews, it illustrates how algorithmic infrastructures, technological affordances, and family dynamics jointly shape elderly users’ digital engagement. The findings confirm that technological empowerment in later life is deeply mediated by social hierarchies and platform logics, challenging assumptions of technology as a neutral enabler. A central observation is the gendered differentiation in creative practices. Male participants tended to pursue technically sophisticated production styles, drawing on functions such as dynamic tracking, multi-layer editing, and advanced transitions. These practices resonated with Douyin’s algorithmic reward mechanisms and enhanced male creators’ visibility and authority in family contexts, echoing Bourdieu’s ( 2001 ) framework of symbolic capital conversion. Female participants, in contrast, preferred narrative templates and emotionally expressive styles, often focusing on private sharing to maintain familial intimacy. This reinforces Faulkner’s (2001) analysis of persistent gendered associations between rationality and technical skill, while also demonstrating that Douyin’s algorithmic classification and recommendation systems privilege production modes aligned with youth-oriented aesthetics. Such dynamics reveal how gender norms are encoded and reproduced through technological infrastructures, confirming Noble’s ( 2018 ) critique of algorithmic bias as a form of structural inequality. The data further indicate that the platform’s automated classification of content containing age-related identifiers significantly suppresses visibility, regardless of production quality. Videos explicitly tagged with terms like “elderly” or “retired” achieved disproportionately low exposure, underscoring Bowker and Star’s ( 1999 ) argument that classification systems are instruments of power that encode and perpetuate social hierarchies. These findings complicate Silverstone and Hirsch’s ( 1994 ) notion of technology domestication as a mutually adaptive process; under platform capitalism, domestication is entwined with commodification and data extraction, as creators adapt their content not solely for expression but to optimize algorithmic visibility. The labor of adjusting filters, keywords, and stylistic features to satisfy platform metrics exemplifies Fox’s ( 2017 ) concept of digital labor, in which creative engagement becomes an avenue for commercial data generation. Intergenerational dynamics add a further layer of complexity. Younger family members often assume advisory or evaluative roles, leveraging algorithmic metrics as indicators of quality. Elderly creators, in turn, navigate these assessments to maintain respect and connection, resulting in a form of “algorithmic filial ethics,” where authority and care are renegotiated in digitally mediated spaces. This dynamic aligns with broader cultural narratives of filial piety while reframing family interaction as a negotiation of algorithmic literacy, technological competence, and social recognition. Theoretically, this study contributes in three ways. First, it extends intersectionality theory (Crenshaw, 1989 ) by demonstrating how computational systems embed and amplify overlapping disadvantages of age and gender, resulting in algorithmic discrimination. Second, it revises technology domestication theory by illustrating that domestication under platform capitalism is characterized by asymmetrical control and commodification, rather than reciprocal adaptation. Third, it illuminates family media practices as sites of cultural renegotiation, where digital literacy and algorithmic performance function as forms of symbolic capital, reshaping intergenerational power relations. Together, these contributions provide a nuanced understanding of how platform infrastructures shape both creative autonomy and identity negotiation in later life. These findings have broader relevance for understanding algorithmic governance and user participation across digital platforms. Addressing algorithmic inequalities requires a critical review of platform design and governance, particularly the opacity of recommendation mechanisms. More inclusive design strategies could incorporate features tailored for elderly users, such as accessible typography, regional dialect voiceovers, and customizable templates that reflect diverse cultural identities (Faulkner, 2001). Additionally, transparency regarding traffic allocation and content classification could mitigate systemic bias, aligning with regulatory frameworks such as the EU’s Digital Services Act. Family-based digital literacy programs and collaborative creation workshops also hold promise for reducing stereotypes and fostering cross-generational understanding. These measures underscore the importance of viewing platforms not merely as technological tools but as cultural infrastructures with profound implications for representation and participation. 6. Limitations The sample was drawn primarily from highly educated, urban-based older adults in Hangzhou, Zhejiang Province, with 70% holding undergraduate or higher degrees. This concentration of participants with advanced digital literacy and family resources limits the representativeness of the findings and constrains the applicability of conclusions to rural, less-educated, or economically disadvantaged groups. The study was also confined to a single platform ecosystem—Douyin and its affiliated editing tool CapCut—providing depth of analysis at the expense of breadth across China’s diverse short video environments, such as Kuaishou or WeChat Channels. Comparative cross-platform research is needed to capture variations in algorithmic logics and user practices in other ecosystems. Fieldwork and interviews were conducted between February and October 2024, which introduces potential temporal bias, as Douyin’s recommendation algorithms, policies on senior creators, and audience behaviors are subject to rapid change. Longitudinal research would help address this limitation and strengthen the robustness of conclusions. Additionally, while this study integrates quantitative content analysis, interface walkthroughs, and qualitative interviews, it remains exploratory in scope. Expanding to larger-scale mixed-methods approaches and computational techniques, including large-sample algorithm audits or social network analysis, could more systematically evaluate the structural embedding of algorithmic bias. Future studies should also situate intersectional discrimination within different cultural and policy contexts, recognizing that algorithmic systems operate within sociotechnical environments deeply shaped by history and culture (Bowker & Star, 1999 ; Noble, 2018 ). Declarations Ethical statements In accordance with the "Ethical Review Measures for Life Sciences and Medical Research Involving Humans" issued by the Central People's Government of the People's Republic of China (Document No. GWKJFA [2023] No. 4), ethical review work must be conducted for research involving human life sciences and medicine. For humanities and social sciences research (excluding neurophysiological experiments), centralized approval from an Institutional Review Board (ethics review committee) is not mandatory, but internal ethical oversight is required. To ensure the ethical standards and rigor of this study, the Ethics Committee of the School of Law and Humanities, Zhejiang Sci-Tech University, conducted a formal review and approved this research on January 15, 2024. The approved study, titled "From being seen to being coded: Technological practices and intergenerational interactions of older short video creators." This research involved in-depth interviews with adult participants and did not include any biomedical experimentation. All participants were fully informed about the study’s objectives, procedures, and data management protocols. Written informed consent was obtained from every participant between February and October 2024, and all participants were explicitly informed of their right to withdraw from the study at any time without penalty. All personal information and identifiers were removed from the data to ensure participant anonymity and confidentiality.. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (2013 revision), ensuring respect, fairness, and protection of participants’ rights. Funding Declaration This work was supported by National Social Science Fund of China (Grant No. 24CXW037); Zhejiang Provincial Education Science Planning Project (Grant No. 2023SCG338); Research Project of Zhejiang Federation of Humanities and Social Sciences (Grant No. 2025N113). References Bivens, R. (2017). The gender binary will not be deprogrammed: Ten years of coding gender on Facebook. 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(Eds.). (1994). Consuming Technologies. Taylor & Francis. Sinanan, J., & Horst, H. A. (2021). Gendered and generational dynamics of domestic automations. Convergence, 27 (5), 1238-1249. https://doi.org/10.1177/1354856520938602 Song, M., & Xing, Y. (2024). The "Snail Girl" in the digital age: Research on domestic digitalization and the invisibilization of women's labor. China Youth Study, (2), 67-76. https://doi.org/10.19633/j.cnki.11-2579/d.2024.0019 Sun, R. (2024). Digital divide and digital feedback: Exploring the impact of new media on intergenerational interaction in rural families. Aging Research, 11 (4), 1454-1461. https://doi.org/10.12677/ar.2024.114207 Tian, Y. (2022). A brief analysis of China's issue of growing old before getting rich and its impact. Social Science Frontiers, 11 (2), 580-585. Van Dijk, J. A. (2006). Digital divide research, achievements and shortcomings. Poetics, 34 (4-5), 221-235. Vera, J. A., McDonald, D. W., & Zachry, M. (2024). How-To in Short-Form: A Framework for Analyzing Short-Format Instructional Content on TikTok. Technical Communication, 71 (2), 5-25. Wajcman, J. (2006). Technocapitalism meets technofeminism: Women and technology in a wireless world. Labour & Industry: A Journal of the Social and Economic Relations of Work, 16 (3), 7-20. https://doi.org/10.1080/10301763.2006.10669327 Wang, C. H., & Wu, C. L. (2022). Bridging the digital divide: The smart TV as a platform for digital literacy among the elderly. B ehaviour & Information Technology, 41 (12), 2546-2559. https://doi.org/10.1080/0144929X.2021.1928750 Wang, M., & Li, Y. (2022). Digital reciprocation and reciprocation resistance: New media use in intergenerational family interaction. Journal of Guangzhou University (Social Science Edition), 21 (1), 77–90. Wang, M., & Li, Y. (2024). Doing age: The cultural industrial production of age imagery-Based on observations of elderly video bloggers. 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Introduction","content":"\u003cp\u003eAmidst the accelerating global trend of population ageing, China is confronting a structural challenge of \u0026ldquo;growing old before getting rich\u0026rdquo; (Tian, 2022). By the end of 2024, the population aged 60 and above in China had reached 313 million, accounting for 22% of the total population, a figure that continues to rise (Jin, 2025). Consequently, \u0026ldquo;active ageing\u0026rdquo; has become a central strategic issue at the national level. The Chinese government\u0026rsquo;s initiative to \u0026ldquo;build an age-friendly digital society\u0026rdquo; aims to bridge the digital divide for older adults through technological empowerment (Gao, 2024). However, existing empirical studies have largely framed older adults as passive or disadvantaged users of technology, often overlooking their creative agency in digital cultural production.\u003c/p\u003e\n\u003cp\u003eAccording to recent data, individuals aged 50 and above constitute 34.1% of China\u0026apos;s internet user base, with over 70% engaging with short video platforms (CINIC, 2024). Platforms such as Douyin, Kuaishou (Kwai), and WeChat Channels have created new avenues for older adults to express themselves. Influential accounts like \u0026ldquo;Kangkang and Grandpa,\u0026rdquo; \u0026ldquo;Fashion Granny Squad,\u0026rdquo; and \u0026ldquo;Grandma Wang\u0026rdquo; have garnered millions of followers by sharing daily life, talents, and stories, thereby challenging stereotypes that associate ageing with digital marginality. More importantly, these practices extend beyond mere technological use; they are deeply intertwined with emotional expression, intergenerational bonding, and the reconfiguration of self-worth.\u003c/p\u003e\n\u003cp\u003eCapCut, the core video-editing tool developed by ByteDance and integrated into Douyin, has played a pivotal role in this shift. Since its release in 2019, CapCut has evolved into a powerful yet accessible mobile and desktop editing suite. It integrates non-linear editing functions (e.g., multi-track timelines, speed modulation), AI-assisted features (e.g., automatic subtitles, template matching), and creative modules (e.g., dynamic stickers, multilingual font libraries, voice modulation). With 371\u0026nbsp;million cumulative downloads and over 800\u0026nbsp;million monthly active users by Q3 2024, CapCut has driven the global expansion of ByteDance\u0026rsquo;s ecosystem, with international versions contributing over 60% of total revenue (Zhang, 2023). Its low technical threshold through tools such as \u0026ldquo;auto-generate clips,\u0026rdquo; \u0026ldquo;text-to-video,\u0026rdquo; AI voice separation, and smart editing has enabled older users to transition from passive consumers to active content producers, giving rise to a new wave of \u0026ldquo;silver digital culture.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThis shift exemplifies how technology can empower individual storytelling and foster intergenerational reciprocity. Younger family members often help older relatives navigate digital tools, while older users employ short videos to document familial events, such as grandchildren\u0026rsquo;s milestones or holiday gatherings, thereby reinforcing emotional ties (Bucher, 2018).\u003c/p\u003e\n\u003cp\u003eHowever, tensions persist between the optimistic narrative of technological empowerment and the lived experiences of older users. Most short video platforms are algorithmically optimized for younger demographics, potentially embedding ageist biases into content moderation and recommendation systems. Studies have shown that content tagged with \u0026ldquo;elderly\u0026rdquo; or \u0026ldquo;silver\u0026rdquo; is more likely to be downranked in algorithmic visibility, especially when produced by older women (Sinanan \u0026amp; Horst, 2021). Simultaneously, the principle of technological neutrality remains a myth, which means design processes are embedded with gendered assumptions extending from user profiling and marketing strategies to interface design. (Song \u0026amp; Xing, 2024; Yang, 2024). Wajcman (2006), for instance, argues that domestic technology use reflects household power structures, with men typically dominating decision-making and women relegated to maintenance tasks.\u003c/p\u003e\n\u003cp\u003eThis gendered encoding of technology is also evident in commercial platforms. MacLeod and McArthur (2019), through critical interface analysis, demonstrate how dating apps like Tinder and Bumble reinforce binary gender norms through data fields (e.g., emphasizing female appearance), interaction rules (e.g., pseudo-empowerment of women messaging first), and algorithmic matching systems. Such studies reveal that seemingly \u0026ldquo;objective\u0026rdquo; technological standards can function as masked carriers of gendered power, thus reproducing hegemonic norms under the guise of neutrality.\u003c/p\u003e\n\u003cp\u003eIn this light, technological design not only reproduces the social construct of \u0026quot;technological masculinity\u0026quot; but also transforms digital literacy into a gendered resource of domestic power. Within family settings, this results in subtle yet persistent forms of power negotiation. Given the growing participation of older adults in short video content creation, this study explores whether gender differences influence how adult children perceive and evaluate their parents\u0026apos; digital outputs, interpreting these evaluations as potential sites of implicit intergenerational negotiation.\u003c/p\u003e\n\u003cp\u003eExisting studies in China on older adults\u0026rsquo; engagement with digital platforms remain limited. Some research has examined how usability and family support facilitate technological adoption among the elderly (Cao \u0026amp; Wang, 2024; Xu \u0026amp; Zeng, 2025), yet these often underemphasize the creative and strategic use of technology. Others highlight how older influencers resist age-related stigma through digital labor (Wang \u0026amp; Ming, 2024), but tend to treat older adults as a homogeneous group, lacking intersectional insights into gendered experiences. While studies on the social construction of technology have illuminated the co-constitution of tools and gender identity (Oudshoorn, 2003), they remain inadequate in addressing the dynamic interplay of ageing, technology, and intergenerational power in the current digital ecosystem. Moreover, conceptual models of \u0026ldquo;digital reciprocity\u0026rdquo; are often reduced to unidirectional flows from tech guidance to emotional feedback, without accounting for the evolving dynamics of family power or gendered asymmetries. The compounding effects of algorithmic ageism and gender bias, which can reinforce gender scripts through the interface design of both \u0026ldquo;tech-driven\u0026rdquo; and \u0026ldquo;emotion-driven\u0026rdquo; editing apps, remain overlooked in existing analytical frameworks.\u003c/p\u003e\n\u003cp\u003eAs short videos increasingly mediate cross-generational and cross-class communication, reshaping the lived experience of ageing in a media-saturated society, this study raises three central research questions:\u003c/p\u003e\n\u003cp\u003e1) Do older male and female users demonstrate differentiated patterns in their use of short video production tools?\u003c/p\u003e\n\u003cp\u003e2) Do digital tools serve as mediators for negotiating power in intergenerational relationships?\u003c/p\u003e\n\u003cp\u003e3) How do algorithmic mechanisms intersect with age and gender to shape the experiences and outcomes of older users\u0026rsquo; digital participation?\u003c/p\u003e\n\u003cp\u003eTo answer these questions, this study integrates intersectionality theory with technogender frameworks to uncover both the structural constraints and agentive strategies in older adults\u0026rsquo; digital practices. Empirically, it focuses on students enrolled in short video production courses at the University for Retired Cadres in Hangzhou, Zhejiang Province. The following sections offer a theoretical review of affordances, gendered technological construction, domestication theory, and intergenerational reciprocity. The methodology outlines the research setting, data collection, and analytical procedures. The subsequent sections present findings and discussions, followed by theoretical contributions, policy implications, and suggestions for future research. This study ultimately seeks to offer new insights into age-inclusive tool design, intergenerational communication, and algorithmic governance in the context of digital ageing.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Technological affordances and the structural embedding of gendered practices\u003c/h2\u003e\u003cp\u003eThe concept of 'affordance,' first introduced by ecological psychologist James Gibson (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), refers to the perceived and actual possibilities for action that an environment offers to an organism. Affordances highlight the inseparable relationship between the actor and the environment, serving as a foundational idea for understanding how technologies shape social behavior. When adopted into the sociology of technology, Hutchby (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) extended the concept to include media and digital tools, arguing that while technologies enable certain actions, such as social media platforms allowing users to share content, they simultaneously constrain user behavior through embedded limitations, including character counts or template restrictions.\u003c/p\u003e\u003cp\u003eIn this duality of enablement and restriction, Markus and Silver (2008) further observed that affordances are often guided by subtle visual and symbolic cues, such as button size, color, or textual guidance, which shape user interaction and implicitly convey the designer\u0026rsquo;s expectations. Hence, technology design is not a neutral \u0026ldquo;toolbox\u0026rdquo; but an active mediator in structuring user behavior and social norms. Interfaces may appear to offer freedom of use while in reality embedding prescribed behavioral scripts within the user experience.\u003c/p\u003e\u003cp\u003eWhen examining the intersection of technology and gender, scholars have revealed how design assumptions can reinforce prevailing gender norms. For example, Bivens (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that Facebook\u0026rsquo;s initial gender settings, which only included \"male\" or \"female,\" marginalized non-binary users. Even after introducing over 50 gender identity options in 2015, the platform continued to algorithmically classify non-binary individuals into residual categories such as \u0026ldquo;others,\u0026rdquo; reinforcing a binary framework in ad targeting and user segmentation. Similarly, Schwartz and Neff (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in their study of Craigslist, demonstrated how interface design, such as pink header bars and default prompts like \u0026ldquo;seeking gentlemen,\u0026rdquo; replicated traditional gender hierarchies by positioning women as passive and men as active.\u003c/p\u003e\u003cp\u003eTechnological systems, therefore, act not only as instruments of functionality but also as enforcers of gender norms. Wajcman (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Oudshoorn (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) both emphasized that technology design and development have historically been male-dominated, resulting in tools and systems that reflect male-centric assumptions. For instance, early office technologies and computing systems were designed with male professionals in mind, while even technologies like contraceptives often reduced women\u0026rsquo;s reproductive health needs to a matter of birth control, illustrating a form of technological disciplining of the female body.\u003c/p\u003e\u003cp\u003eIn the context of new media, Pan and Liu (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) proposed a tripartite model of affordances: information production affordance, social affordance, and mobility affordance. These dimensions reflect how affordances in digital platforms are not static but dynamically embedded in design choices, algorithmic configurations, and interactive rules, all of which participate in reproducing cultural norms and structural inequalities, including those based on gender. Thus, from ecological psychology to media interface studies, the affordance framework reveals how technology, while offering possibilities for action, also reinforces patterns of identity and power.\u003c/p\u003e\u003cp\u003eGrounded in this theoretical lineage, this study explores how older adults\u0026rsquo; engagement with short video production tools is shaped by gendered affordances and investigates whether such tools reinforce or subvert normative gender expectations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRQ1\u003c/strong\u003e\u003cp\u003eDo older male and female users demonstrate differentiated patterns in their use of short video production tools?\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Domestication Theory and Intergenerational Negotiation of Technology\u003c/h2\u003e\u003cp\u003eWhile technological affordances may appear neutral, they are often shaped by cultural assumptions and social power relations. The relationship between users and technologies is not one-directional; rather, it involves dynamic negotiation. Domestication theory, introduced by Silverstone and Hirsch (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), conceptualizes how users integrate technologies into everyday life through appropriation, objectification, incorporation, and conversion, thereby assigning personal and cultural meanings to technologies. This framework shifts attention from design to use, emphasizing user agency and contextual adaptations.\u003c/p\u003e\u003cp\u003eBuilding on this, Lie and S\u0026oslash;rensen (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) argued that domestication is a bidirectional process. Although users operate within the functional constraints of a given technology, they can also repurpose it in creative ways, thereby reprogramming its intended use. Haddon (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) documented how youth often \u0026ldquo;hack\u0026rdquo; or modify software to personalize functions beyond design intent, demonstrating that scripts embedded in technology are not fixed, but modifiable. Similarly, Hamraie (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), in disability studies, noted how unintended uses, such as using voice assistants for daily life support, reveal the emancipatory potential of reappropriating technologies, especially for marginalized users.\u003c/p\u003e\u003cp\u003eThis logic extends to intergenerational media practices. Early studies of the digital divide (e.g., Van Dijk, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) portrayed older adults as \u0026ldquo;technologically deficient,\u0026rdquo; in contrast to younger \u0026ldquo;digital natives.\u0026rdquo; However, this linear perspective has been increasingly challenged. For instance, Wang and Wu (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that digital guidance within families is rarely unidirectional. While children may offer technical instruction with a sense of superiority, older parents often draw on moral authority to assert influence in familial decision-making. Similarly, Livingstone and Helsper (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) observed that youth providing digital support often assert autonomy, transforming technical interactions into tools of intergenerational power negotiation.\u003c/p\u003e\u003cp\u003eThese insights highlight the limitations of portraying older adults as passive or homogenous. Hargittai and Shafer (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) found that although gender differences exist in self-reported digital skills, older users exhibit high levels of adaptability and strategic use in practice, indicating the presence of \u0026ldquo;digital wisdom\u0026rdquo; that is often overlooked in mainstream accounts.\u003c/p\u003e\u003cp\u003eIn the Chinese context, such intergenerational interactions are often embedded in Confucian values of filial piety. Wang and Li (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) described the phenomenon of \u0026ldquo;digital reciprocity,\u0026rdquo; wherein younger family members provide tech support out of ethical obligations, yet use this process to assert epistemic authority in the household. The dynamic is thus fraught with both emotional labor and power asymmetries. Drawing on Foucault\u0026rsquo;s (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) notion of micro-power, even seemingly benign acts, such as hands-on instruction, can serve as disciplinary mechanisms that subtly shape older adults\u0026rsquo; behaviors and communication styles. Here, digital tools become intermediaries for everyday power negotiations.\u003c/p\u003e\u003cp\u003eFrom domestication to reappropriation to intergenerational negotiation, these studies underscore that technologies are embedded in complex sociocultural structures. Within familial contexts, technology use becomes a site where emotions, authority, and care ethics intersect.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRQ2\u003c/strong\u003e\u003cp\u003eDo digital tools serve as mediators for negotiating power in intergenerational relationships?\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Algorithmic Discipline and the Intersecting Exclusions of Age and Gender\u003c/h2\u003e\u003cp\u003eTo unpack the entanglement of identity and technological systems, this study draws on Crenshaw\u0026rsquo;s (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) theory of intersectionality. Initially applied to highlight how Black women experience oppression differently from both white women and Black men, intersectionality reveals how multiple systems of inequality intersect to produce unique forms of marginalization. This lens is particularly relevant in the digital age, where algorithmic systems encode and amplify structural biases.\u003c/p\u003e\u003cp\u003eGullette (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) argued that digital media and intelligent technologies frequently embed ageist assumptions, casting older users as cognitively or physically incapable. This is especially true for older women, whose content is often algorithmically deprioritized due to the double burden of age and gender. Noble\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) seminal work, Algorithms of Oppression, exposed how seemingly neutral search and recommendation systems reproduce racial and gender hierarchies through historical data patterns. The personalization and automation of such systems cloak their biases in a veil of legitimacy.\u003c/p\u003e\u003cp\u003eBucher (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasized the normativity of algorithmic logic, where \u0026ldquo;if\u0026ndash;then\u0026rdquo; models favor content that is emotionally intense, visually stimulating, and highly interactive. This bias systematically disadvantages certain content creators, including many older users, whose work may be less aggressive or technically polished. Zuboff\u0026rsquo;s (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) concept of \u0026ldquo;surveillance capitalism\u0026rdquo; further critiques how even acts of refusal, such as opting out of personalization, are harvested as behavioral data, thereby reinforcing algorithmic control. As a result, older adults\u0026rsquo; sporadic interaction patterns are often flagged as \u0026ldquo;low-value,\u0026rdquo; which marginalizes them within mainstream digital visibility.\u003c/p\u003e\u003cp\u003eFuchs (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) critiqued digital labor economies, arguing that users\u0026rsquo; unpaid content production serves as a resource for platform monetization. For older creators, this means that their contributions are algorithmically harvested without corresponding rewards, reinforcing their instrumentalization within platform capitalism.\u003c/p\u003e\u003cp\u003eTaken together, algorithmic design constitutes a space of intersecting structural exclusions. First, affordance structures reproduce gendered interactional norms. Second, algorithmic content evaluation reinforces ageist mechanisms of visibility. For older female creators, short video expression becomes both a site of adaptation and a field of resistance. This study, therefore, asks:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRQ3\u003c/strong\u003e\u003cp\u003eHow do algorithmic mechanisms intersect with age and gender to shape the experiences and outcomes of older users\u0026rsquo; digital participation?\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.1 Research field and participants\u003c/h2\u003eThis study focuses on a short video production course offered by the Hangzhou University for Retired Cadres, with particular attention to students’ use of CapCut, a short-form video editing tool under the Douyin (TikTok China) ecosystem. Participant selection criteria included: (1) consistent use of CapCut for at least three months, and (2) an average output of no fewer than two short videos per month. Additionally, video content had to center around family life or exhibit clear gendered features (e.g., male users favoring technical editing functions, female users emphasizing emotional storytelling). After providing detailed explanations of research objectives and securing informed consent, 30 core participants (15 male, 15 female) were selected. All participants voluntarily took part in this study. Prior to participation, they were fully informed of the research objectives, procedures, and data usage. Written informed consent was obtained from each participant, and they were assured of their right to withdraw from the study at any time without any consequences. All data were anonymized to protect personal privacy.\u003cp\u003eThis field site and participant group offer several notable advantages for exploring domestication processes: (1) The course delivered systematic training in short video production using CapCut from February to July 2024. Participants completed at least 10 videos covering diverse themes—family documentation (e.g., grandchildren’s milestones), skill tutorials (e.g., calligraphy, dance, knitting), and local culture (e.g., Hangzhou landscapes)—offering rich empirical material for analyzing the dynamic process of technological domestication; (2) The participant pool includes 72 men (48%) and 78 women (52%), aged between 53 and 65, with educational backgrounds ranging from middle school to postgraduate level. Their professional experiences span teaching, engineering, medicine, and public service, reflecting the heterogeneity typical in aging and media research; and (3) Approximately 85% of participants actively engage with children and grandchildren via family WeChat groups and Douyin accounts. These communication channels, such as comment sections, private messages, and offline discussions, provide a natural setting to observe dynamics of digital reverse mentoring and intergenerational power negotiation (Sun, 2024).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.2 Methodology and operational steps\u003c/h2\u003e\n \u003cp\u003eDrawing on an intersectional theoretical framework, this study adopts a multi-method approach to investigate the interplay between age, gender, technological design, and cultural practice. Specifically, the research integrates: (1) Technological affordance analysis through interface walkthroughs; (2) User experience interpretation through in-depth interviews; (3) Semiotic and behavioral verification through short video content analysis.\u003c/p\u003e\n \u003cp\u003eGiven that CapCut is deeply embedded within the Douyin platform, the interface design of the editing tool cannot be separated from broader algorithmic and content distribution logics, thereby forming a hybrid production environment characterized by tool–platform entanglement.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCapCut interface walkthrough\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBuilding on the walkthrough methodology proposed by Light et al. (2018), this study explores the interaction chain spanning interface design, user behavior, and cultural scripts within CapCut. Research shows that gendered practices are often subtly encoded in interface affordances, functionality structures, and algorithmic nudges (Sinanan \u0026amp; Horst, 2021).\u003c/p\u003e\n \u003cp\u003eThe walkthrough followed three operational steps: (1) Function mapping: Identify gendered functional zones, such as the “tech-focused” module (e.g., keyframes, masking, multi-track editing, chroma key) and the “emotion-oriented” module (e.g., family templates, retro filters, animated typography). (2) Script decoding: Analyze age- and gender-coded cues in interface language (e.g., “One-click auto-edit” signaling efficiency bias; “Capture heartwarming moments” emphasizing emotional labor) and assess how these scripts guide behavior. (3) Simulated user pathing: Create senior user profiles and document the entire “registration–creation–publishing–interaction” workflow to detect design biases (e.g., default templates privileging youth aesthetics) and examine how these interact with Douyin’s algorithmic recommendations..\u003c/p\u003e\n \u003cp\u003eAs shown in Table 1, a structured framework guided data collection and analysis:\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eWalkthrough framework for CapCut interface\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eAnalytical Dimension\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eOperational Definition\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eData Collection Method\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFunctional Zone Mapping\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCategorize CapCut modules (e.g., technical/emotional), record feature names, icons, and hierarchy\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInterface screenshots, user pathway logs, feature usage statistics\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGendered Design Clues\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAnalyze interface copy (e.g., “One-click edit”) and symbolic elements (e.g., color, avatar style)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eTextual analysis, visual semiotic coding\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAge Inclusivity Evaluation\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAssess senior-friendly design (e.g., font size, instructional prompts) and usability thresholds\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAccessibility testing, learning cost scoring\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAlgorithmic Logic Simulation\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eUse controlled experiments (e.g., tech vs. emotional styles) to infer recommendation biases\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMonitoring engagement metrics, reverse-engineering platform logic\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eIn-Depth Interviews with Older Users\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo challenge the homogeneous depiction of older users in digital studies, this study conducted in-depth interviews to explore how gender and age shape the use of CapCut and Douyin for video production. Interviews were conducted with the 30 selected participants (15 men, 15 women) between February and October 2024, with informed consent and audio recording permissions obtained.\u003c/p\u003e\n \u003cp\u003eThe interview protocol was structured around four dimensions (see Table 2):\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFour-dimensional mixed-method framework\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eDimension\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eOperational Definition\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eData Sources\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTechnological Domestication\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGendered feature preferences and learning barriers\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInterviews, walkthrough logs\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIntergenerational Negotiation\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFeedback types (technical/emotional) and household division of labor\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInterviews, family chat logs\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAlgorithmic Response\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAdaptive strategies to platform rules (e.g., hashtag manipulation)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eContent analysis, platform data\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLocal Cultural Scripts\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInfluence of filial piety and face-saving norms on tech critique\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInterviews, field notes\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003eEach dimension informed the design of interview questions. Examples include: (1) Domestication: “Why do you prefer certain features (e.g., keyframes)? Do you think some functions are gender-biased?”and“What were your biggest challenges in learning CapCut?”(2) Intergenerational Negotiation:“How do your children respond to your videos? Has their feedback influenced your editing decisions?”and“Does the division of labor in video production reflect traditional gender roles?”(3) Algorithmic Response:“What do you do when your videos don’t get views? Have you changed your hashtags or style?” and “Are you aware of age-related algorithmic suppression?”(4) Cultural Scripts: “How does filial piety affect your willingness to discuss platform biases with your children?”\u003cp\u003eA constructivist thematic analysis was conducted, enhanced by a theory-layered coding scheme: (1) Open Coding: Extracted behavioral tags (e.g., “tag optimization,” “tech assistance frequency”) without imposing theoretical labels. (2) Axial Coding: Clustered tags into the four analytic dimensions (e.g.,“child tech guidance”under intergenerational negotiation). (3) Selective Coding: Modeled interrelations (e.g., “domestication–algorithmic response” reveals how tool mastery drives platform adaptation). (4) Theory Layer: Interpreted dynamics through domestication theory, intersectionality, and Confucian ethics, forming a localized analytic paradigm.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eShort Video Content Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo validate users’ editing practices and expression styles, the study conducted a content analysis of 150 family-themed Douyin videos, randomly sampled from the 30 participant accounts (75 videos by men, 75 by women). Guided by Vera et al. (2024), videos were coded across three key dimensions: technical complexity, emotional expression, and intergenerational interaction.\u003c/p\u003e\n \u003cp\u003eCoding categories included: (1) Technical Complexity: Number of special effects (e.g., split-screen, stickers), transition types (e.g., dissolve, slide), and originality of subtitles (scored 0–3). (2) Emotional Expression: Duration of sentimental background music, presence of intimate shots (e.g., hugs, close-ups), and nostalgic elements (e.g., black-and-white filters, old photos). (3) Intergenerational Interaction: Sentiment polarity in children’s comments (positive/neutral/negative), presence of tech-help requests, and emotional feedback intensity (based on emoji and exclamatory usage). See Table 3.\u003c/p\u003e\n \u003cp\u003eAdditional metrics such as completion rate, average watch time, and engagement (likes, comments, shares) were recorded. To ensure inter-rater reliability, two coders underwent 15 hours of training and conducted a pilot test (\u003cem\u003en\u003c/em\u003e = 20). Final Cohen's Kappa reached 0.82, indicating strong coding consistency.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCoding framework for Douyin video content\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eAnalytical Dimension\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eSpecific Indicator\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eOperational Definition\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eExample\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eTechnical Complexity\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSpecial Effects\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCount of effects per video (e.g., split-screen, stickers)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1 split-screen + 3 stickers = 4\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eTransitions\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eType and count of transitions\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2 dissolve transitions = dissolve × 2\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSubtitle Originality\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0 = no subtitles; 1 = default; 2 = modified; 3 = fully original\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ePersonalized poetry = score 3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eEmotional Expression\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSentimental Music\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eDuration ratio of sentimental music\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e80s/90s = 88.9%\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIntimate Shots\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNumber of warm interactions\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3 hugging close-ups = 3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNostalgic Symbols\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFrequency of nostalgic elements (0–2 scale)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eB\u0026amp;W filter + 2 old photos = 2\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eIntergenerational Interaction\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eComment Sentiment\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCoded as positive/neutral/negative\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e“Mom, this moved me” = positive\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eTech-Help Requests\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCount of guidance-seeking comments\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e“How to do this transition?”\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEmotional Feedback\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBased on emoji/exclamation marks: 0 = none; 1 = 1–2; 2 = 3+\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eComment “heart and crying” emojis = 2\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\n\u003c/div\u003e"},{"header":"4. Analysis and Findings","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1) Gendered Technological Practices: Differentiation of Elderly \u0026ldquo;Digital Gestures\u0026rdquo; on the CapCut Platform\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis begins by examining how elderly men and women navigated CapCut and its integration with Douyin, focusing on the gendered dynamics shaping their editing practices, feature choices, and creative approaches. Although all participants were trained under an identical curriculum and completed the same video production tasks, their engagement with the platform\u0026rsquo;s tools and affordances displayed distinct patterns. These differences mirror broader scholarly discussions of gendered relationships with technology, particularly within caregiving and educational settings (Lunt \u0026amp; Livingstone, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e; Silverstone \u0026amp; Hirsch, \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e; Lie \u0026amp; S\u0026oslash;rensen, \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e; Haddon, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOur quantitative analysis reveals significant contrasts in tool usage and stylistic orientation between genders. Among the 15 male participants, 13 regularly employed advanced editing features\u0026mdash;such as multi-track editing, keyframe manipulation, transition effects, and precise background music adjustment\u0026mdash;producing an average of 4.2 high-level effects per video (SD\u0026thinsp;=\u0026thinsp;1.3). In comparison, the female participants averaged 1.8 high-level effects per video (SD\u0026thinsp;=\u0026thinsp;0.7), a difference that was statistically significant (t\u0026thinsp;=\u0026thinsp;6.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Marked disparities were also observed in the adoption of specific techniques: split-screen (\u0026chi;\u0026sup2; = 18.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), masking (\u0026chi;\u0026sup2; = 24.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and keyframes (\u0026chi;\u0026sup2; = 31.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For instance, Participant M09 (62, retired engineer) combined masking and keyframe functions to create a \u0026ldquo;grandson growth comparison\u0026rdquo; video, layering photographs with fade-ins and synchronized background music. He commented, \u0026ldquo;It must be like an engineering drawing, aligned layer by layer, or it won\u0026rsquo;t look professional.\u0026rdquo; This video received a complexity score of 8.7/10, far exceeding the female participants\u0026rsquo; mean score of 4.2 (\u0026chi;\u0026sup2; = 34.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These patterns reflect a gendered orientation in which male participants approached video editing as an exercise in precision and technical control, consistent with prior observations of \u0026ldquo;instrumentality\u0026rdquo; in technology appropriation (Silverstone \u0026amp; Hirsch, \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e; Haddon, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBy contrast, female participants gravitated toward features prioritising emotional resonance and aesthetic effects. Twelve of the 15 women reported frequent reliance on pre-set templates, filters, beautification tools, animated captions, and automatically matched music. Their content tended to emphasise family moments, intimate memories, and everyday crafts, creating a \u0026ldquo;warm atmosphere\u0026rdquo; through soft lighting and narrative text. F04 (58, retired teacher) explained, \u0026ldquo;I make these videos for my granddaughter; it has to be gentle, since emotion is the most important.\u0026rdquo; These practices illustrate the ways in which women deploy digital tools to sustain relational and identity-based meanings, reinforcing the insights of Silverstone and Hirsch (\u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e) and Lie and S\u0026oslash;rensen (\u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e) on the symbolic dimensions of domesticating media technologies.\u003c/p\u003e\n\u003cp\u003eLearning strategies also displayed gendered patterns. Male participants more often employed exploratory \u0026ldquo;trial\u0026ndash;debug\u0026ndash;optimise\u0026rdquo; methods, repeatedly testing feature combinations to achieve a polished effect. Female participants favoured platform-recommended styles and quick templates, seeking efficiency while preserving emotional expression. This divergence suggests that elderly users\u0026rsquo; creative gestures are shaped not only by interface design and algorithmic recommendation logics but also by intersecting gender identities and social expectations (Crenshaw, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e; Bucher, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThese observed differences may be partially attributed to participants\u0026rsquo; professional backgrounds. Many male respondents had careers in computing, engineering, or management, which facilitated the transfer of technical expertise to short video production. Female participants, however, often came from education, caregiving, or service industries, with comparatively limited prior exposure to digital tools. This uneven \u0026ldquo;technological habitus,\u0026rdquo; as described by Wajcman (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), creates selective affinities for certain features while reinforcing algorithmically mediated user stratification. Aspirations for creative production also differed: four male respondents expressed intentions to monetise or formalise their video-making skills by teaching in senior universities, documenting community events, or running personal channels, whereas female respondents mainly framed video production as a means to share emotions and maintain family bonds, motivated by sentiments such as \u0026ldquo;letting the children see,\u0026rdquo; \u0026ldquo;giving the family a keepsake,\u0026rdquo; or \u0026ldquo;recording life fragments.\u0026rdquo; This confirms that elderly creators actively reinterpret and reconstruct gender roles through their digital practices (Haraway, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eHowever, the study also observed notable cases of boundary-crossing and collaborative learning. F02, for example, mastered advanced voice-over and text animation techniques after intensive practice, subsequently sharing these skills with peers. M07, with guidance from his daughter, experimented with cartoon stickers and soft background music, admitting that while initially hesitant, he came to enjoy these \u0026ldquo;playful\u0026rdquo; features. These examples illustrate that gendered distinctions are not rigid binaries but dynamic, negotiated practices shaped by family collaboration, peer influence, and algorithmic nudging.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e2) Intergenerational Negotiation under Algorithmic Influence: The Gendered Power Structure in Family Feedback\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTo address the second research question, we also explored how elderly users\u0026rsquo; short video practices on Douyin intersect with family dynamics and algorithmic processes, shaping intergenerational relations and power structures. Based on a triangulated dataset comprising 30 family WeChat group conversations, 450 sentiment-coded comments from younger family members, and more than 40 in-depth interviews, the analysis traces the evolving connections between Douyin\u0026rsquo;s recommendation mechanisms, familial discursive patterns, and gendered roles.\u003c/p\u003e\n\u003cp\u003eThe findings reveal a pronounced gender asymmetry in intergenerational feedback and symbolic recognition. Among male respondents, 89% (N\u0026thinsp;=\u0026thinsp;13) frequently received technical inquiries from their children after sharing videos, with requests for editing guidance constituting 37% of all comments. In contrast, only 5% of feedback on women\u0026rsquo;s works involved technical questions, while emotional responses accounted for as much as 68%. For example, M07, a 60-year-old retired teacher, became known among his family for mastering dynamic tracking and split-screen editing to document his grandson\u0026rsquo;s piano practice sessions. Comments on his daughter\u0026rsquo;s WeChat Moments included: \u0026ldquo;My dad\u0026rsquo;s skills could teach a class,\u0026rdquo; alongside inquiries such as \u0026ldquo;What software did you use?\u0026rdquo; and \u0026ldquo;How did you make the transition effects?\u0026rdquo; Similarly, M09 (62, retired engineer) reflected: \u0026ldquo;After learning to edit videos on Douyin, my kids think I\u0026rsquo;ve kept up with the times; now I\u0026rsquo;m the \u0026lsquo;director\u0026rsquo; of our family trips.\u0026rdquo; These narratives suggest that Douyin\u0026rsquo;s algorithmically amplified visibility transforms technical expertise into a form of symbolic capital (Bourdieu, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e), strengthening elderly men\u0026rsquo;s authority and recognition in family hierarchies.\u003c/p\u003e\n\u003cp\u003eThe data also indicate that Douyin\u0026rsquo;s traffic distribution system further magnifies this symbolic value. Technical videos produced by male participants were more likely to enter recommendation streams, reinforcing a cycle of algorithmic endorsement and familial recognition. For instance, M11 (64, retired photographer) created a multi-camera highlight reel of his grandson\u0026rsquo;s football match, which received substantial algorithmic promotion, prompting his children to remark, \u0026ldquo;Dad, your video is trending!\u0026rdquo; Such feedback links algorithmic metrics\u0026mdash;such as views, likes, and recommendations\u0026mdash;with social validation, positioning technological skills as a key currency in intergenerational negotiations.\u003c/p\u003e\n\u003cp\u003eWomen\u0026rsquo;s creative outputs were primarily evaluated within the framework of emotional labor and private domains. Seventy-six percent of female participants (N\u0026thinsp;=\u0026thinsp;11) received predominantly sentimental feedback, with comments like \u0026ldquo;The video is so warm\u0026rdquo; or \u0026ldquo;It made me cry,\u0026rdquo; whereas technical questions were rare. F08\u0026rsquo;s New Year\u0026rsquo;s Eve dinner video, for example, was praised in a private message by her daughter\u0026mdash;\u0026ldquo;Mom, you filmed it so warmly\u0026rdquo;\u0026mdash;but elicited no discussion of captioning or music editing. Female participants also reported their videos circulated mostly within closed WeChat family groups or private Douyin shares, often facing algorithmic throttling due to content classification as \u0026ldquo;emotional.\u0026rdquo; View counts of female-authored videos were approximately one-fifth of comparable male \u0026ldquo;technical\u0026rdquo; content, reflecting how Douyin\u0026rsquo;s implicit classification and recommendation logics systematically privilege technical aesthetics over emotional expression (Noble, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). F06 (61, retired bank executive) captured this tension: \u0026ldquo;Making videos felt like doing homework; my son said I was too sentimental, but when I added effects, he said it looked unnatural.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThese findings demonstrate that algorithmic and familial systems intersect to reproduce traditional gender divisions, framing men\u0026rsquo;s technological practices as forms of public competence and rational authority, while women\u0026rsquo;s outputs are consistently categorized within the realm of domestic caregiving. This dynamic echoes Bourdieu\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) theory of symbolic capital conversion, wherein algorithmically endorsed technical expertise is transformed into authority within family structures. It further supports Noble\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) critique that platform \u0026ldquo;neutrality\u0026rdquo; often masks the systemic reproduction of inequality.\u003c/p\u003e\n\u003cp\u003eA further layer of complexity emerges in how Douyin\u0026rsquo;s design contributes to what can be described as \u0026ldquo;algorithmic filial ethics.\u0026rdquo; Traditional Confucian cultural norms emphasize respect for elders and deference to parental authority; however, Douyin\u0026rsquo;s youthful aesthetic preferences and data-driven ranking mechanisms often invert this hierarchy, positioning younger family members as technical gatekeepers. For example, F10 (58, retired accountant) attempted to experiment with AI-generated messages about her future self but was dismissed by her son as \u0026ldquo;unrealistic,\u0026rdquo; prompting her to revert to default templates. Such moments highlight how algorithmic affordances and intergenerational knowledge disparities jointly regulate elderly creators\u0026rsquo; technical practices, imposing dual pressures of maintaining family \u0026ldquo;face\u0026rdquo; and algorithmic performance metrics. In this sense, children\u0026rsquo;s evaluations of videos\u0026mdash;whether framed as emotional praise or technical critique\u0026mdash;become a measurable form of filial piety, quantified and mediated by platform algorithms.\u003c/p\u003e\n\u003cp\u003eOverall, intergenerational feedback on Douyin constitutes more than casual family commentary; it functions as a discursive arena in which gendered power structures are negotiated and reinforced. Men leverage technological expertise to enhance symbolic authority, while women\u0026rsquo;s creative contributions, though deeply valued emotionally, are constrained to private spheres and deprioritized algorithmically. This reinforces a multi-layered structure of gendered labor, where women assume invisible emotional maintenance roles, and men consolidate leadership through technological performance. In line with Noble\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) argument that algorithmic infrastructures codify existing social hierarchies, Douyin\u0026rsquo;s recommendation system emerges as an active mediator of family politics, shaping intergenerational power negotiations in subtle yet persistent ways. Elderly short video creation, therefore, is not only a form of digital expression but also a critical site for examining how algorithmic systems and traditional cultural expectations co-produce symbolic authority, emotional labor, and identity reproduction in later life.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e3) Structural Dilemmas of Intersectional Empowerment: The Intertwined Effects of Gender and Age in Implicit Algorithmic Logic\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eBuilding on this qualitative foundation, the analysis draws on 50 comparative interface walkthroughs and statistical data from 150 short videos to demonstrate that Douyin\u0026rsquo;s content recommendation framework operates as a classification system in Bowker and Star\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) sense, systematically translating social hierarchies into automated decisions. Videos containing age-related markers such as \u0026ldquo;elderly\u0026rdquo; or \u0026ldquo;retired\u0026rdquo; achieved an average exposure rate of only 14 percent compared with similar content without such identifiers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This disparity is exemplified by participant F05\u0026rsquo;s calligraphy class video, which received only one-fifth the views of comparable videos lacking age labels. These patterns reveal that algorithmic downgrading effectively codes age as a negative attribute, marginalizing elderly creators within Douyin\u0026rsquo;s data ecology and reinforcing their peripheral status as users.\u003c/p\u003e\n\u003cp\u003eGender further amplifies this exclusion, as technical videos by male participants dominate Douyin\u0026rsquo;s recommendation pool and achieve exposure rates 3.2 times higher than those of female creators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For example, M11\u0026rsquo;s football highlight videos, edited with rapid transitions and multiple camera angles, were promoted widely, whereas F08\u0026rsquo;s similarly themed videos were classified as \u0026ldquo;emotional content,\u0026rdquo; limiting their circulation to private networks. This pattern indicates that Douyin\u0026rsquo;s algorithm privileges markers of technical sophistication that align with traditionally masculine forms of cultural capital (Bourdieu, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wajcman, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), thereby reinforcing gender hierarchies in creative labor. Visual aesthetics also serve as a disciplinary tool. CapCut\u0026rsquo;s template library features predominantly youthful imagery, pushing older creators, especially women, to rely on beauty filters and retouching tools to meet audience expectations, often triggering intergenerational critique. F08 remarked, \u0026ldquo;I need to smooth my skin, but not too much or my kids will dislike it,\u0026rdquo; illustrating the tension between authenticity, algorithmic favorability, and family approval. Such mechanisms illustrate that classification systems extend beyond content evaluation, shaping creators\u0026rsquo; visual self-presentation and identity work.\u003c/p\u003e\n\u003cp\u003eAlthough structural disadvantages are deeply embedded in Douyin\u0026rsquo;s algorithmic design, elderly users actively negotiate and resist these constraints through diverse strategies. Some creators enhance the technical complexity of their videos to appeal to the recommendation algorithm, as illustrated by F05\u0026rsquo;s innovative collage of archival photos with AI-generated captions, which achieved high technical complexity scores but remained confined to a private traffic pool due to algorithmic prioritization of identity markers over production quality. Others develop collective approaches, forming informal online and offline networks to share expertise and co-create tools such as regional dialect voiceovers and enlarged subtitles designed to challenge youth-centric norms. These grassroots initiatives provide technical support and represent symbolic resistance, offering alternative forms of expression. A smaller group of participants has begun advocating for algorithmic transparency, calling for disclosure of recommendation standards, the inclusion of age-inclusivity measures, and the establishment of dedicated \u0026ldquo;silver-haired traffic pools.\u0026rdquo; Although these demands have yet to elicit institutional responses, they signal an emerging critical consciousness of algorithmic power and a shift toward collective action (Noble, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNevertheless, the platform\u0026rsquo;s commercial imperatives severely limit the effectiveness of these resistive practices, ultimately imposing emotional strain and identity fragmentation on older creators. Among female participants, 65% reported adopting age-disguising techniques, often describing a felt alienation from their authentic selves. As F02 reflected, \u0026ldquo;I always have to think about how to look younger before uploading videos, feeling like I\u0026rsquo;m not showing my true self.\u0026rdquo; Even male participants, though generally benefiting from algorithmic promotion, expressed anxiety around sustaining production quality. M12 remarked, \u0026ldquo;Editing videos is more exhausting than working before retirement,\u0026rdquo; highlighting the often-invisible affective labor embedded in digital content creation (Fox, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). These experiences demonstrate that empowerment on the platform remains deeply intertwined with surveillance and self-discipline. Each attempt to circumvent algorithmic marginalization\u0026mdash;whether through aesthetic filters, keyword optimization, or technical innovations\u0026mdash;paradoxically generates further behavioral data, thereby reinforcing the platform\u0026rsquo;s classificatory logic and deepening its mechanisms of control.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study examined elderly users\u0026rsquo; short video practices on Douyin and its editing platform CapCut, adopting an intersectionality perspective to reveal the intertwined effects of age, gender, and algorithmic governance. By integrating content analysis, platform walkthroughs, and in-depth interviews, it illustrates how algorithmic infrastructures, technological affordances, and family dynamics jointly shape elderly users\u0026rsquo; digital engagement. The findings confirm that technological empowerment in later life is deeply mediated by social hierarchies and platform logics, challenging assumptions of technology as a neutral enabler.\u003c/p\u003e\u003cp\u003eA central observation is the gendered differentiation in creative practices. Male participants tended to pursue technically sophisticated production styles, drawing on functions such as dynamic tracking, multi-layer editing, and advanced transitions. These practices resonated with Douyin\u0026rsquo;s algorithmic reward mechanisms and enhanced male creators\u0026rsquo; visibility and authority in family contexts, echoing Bourdieu\u0026rsquo;s (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) framework of symbolic capital conversion. Female participants, in contrast, preferred narrative templates and emotionally expressive styles, often focusing on private sharing to maintain familial intimacy. This reinforces Faulkner\u0026rsquo;s (2001) analysis of persistent gendered associations between rationality and technical skill, while also demonstrating that Douyin\u0026rsquo;s algorithmic classification and recommendation systems privilege production modes aligned with youth-oriented aesthetics. Such dynamics reveal how gender norms are encoded and reproduced through technological infrastructures, confirming Noble\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) critique of algorithmic bias as a form of structural inequality.\u003c/p\u003e\u003cp\u003eThe data further indicate that the platform\u0026rsquo;s automated classification of content containing age-related identifiers significantly suppresses visibility, regardless of production quality. Videos explicitly tagged with terms like \u0026ldquo;elderly\u0026rdquo; or \u0026ldquo;retired\u0026rdquo; achieved disproportionately low exposure, underscoring Bowker and Star\u0026rsquo;s (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) argument that classification systems are instruments of power that encode and perpetuate social hierarchies. These findings complicate Silverstone and Hirsch\u0026rsquo;s (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) notion of technology domestication as a mutually adaptive process; under platform capitalism, domestication is entwined with commodification and data extraction, as creators adapt their content not solely for expression but to optimize algorithmic visibility. The labor of adjusting filters, keywords, and stylistic features to satisfy platform metrics exemplifies Fox\u0026rsquo;s (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) concept of digital labor, in which creative engagement becomes an avenue for commercial data generation.\u003c/p\u003e\u003cp\u003eIntergenerational dynamics add a further layer of complexity. Younger family members often assume advisory or evaluative roles, leveraging algorithmic metrics as indicators of quality. Elderly creators, in turn, navigate these assessments to maintain respect and connection, resulting in a form of \u0026ldquo;algorithmic filial ethics,\u0026rdquo; where authority and care are renegotiated in digitally mediated spaces. This dynamic aligns with broader cultural narratives of filial piety while reframing family interaction as a negotiation of algorithmic literacy, technological competence, and social recognition.\u003c/p\u003e\u003cp\u003eTheoretically, this study contributes in three ways. First, it extends intersectionality theory (Crenshaw, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) by demonstrating how computational systems embed and amplify overlapping disadvantages of age and gender, resulting in algorithmic discrimination. Second, it revises technology domestication theory by illustrating that domestication under platform capitalism is characterized by asymmetrical control and commodification, rather than reciprocal adaptation. Third, it illuminates family media practices as sites of cultural renegotiation, where digital literacy and algorithmic performance function as forms of symbolic capital, reshaping intergenerational power relations. Together, these contributions provide a nuanced understanding of how platform infrastructures shape both creative autonomy and identity negotiation in later life.\u003c/p\u003e\u003cp\u003eThese findings have broader relevance for understanding algorithmic governance and user participation across digital platforms. Addressing algorithmic inequalities requires a critical review of platform design and governance, particularly the opacity of recommendation mechanisms. More inclusive design strategies could incorporate features tailored for elderly users, such as accessible typography, regional dialect voiceovers, and customizable templates that reflect diverse cultural identities (Faulkner, 2001). Additionally, transparency regarding traffic allocation and content classification could mitigate systemic bias, aligning with regulatory frameworks such as the EU\u0026rsquo;s Digital Services Act. Family-based digital literacy programs and collaborative creation workshops also hold promise for reducing stereotypes and fostering cross-generational understanding. These measures underscore the importance of viewing platforms not merely as technological tools but as cultural infrastructures with profound implications for representation and participation.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003eThe sample was drawn primarily from highly educated, urban-based older adults in Hangzhou, Zhejiang Province, with 70% holding undergraduate or higher degrees. This concentration of participants with advanced digital literacy and family resources limits the representativeness of the findings and constrains the applicability of conclusions to rural, less-educated, or economically disadvantaged groups. The study was also confined to a single platform ecosystem\u0026mdash;Douyin and its affiliated editing tool CapCut\u0026mdash;providing depth of analysis at the expense of breadth across China\u0026rsquo;s diverse short video environments, such as Kuaishou or WeChat Channels. Comparative cross-platform research is needed to capture variations in algorithmic logics and user practices in other ecosystems.\u003c/p\u003e\u003cp\u003eFieldwork and interviews were conducted between February and October 2024, which introduces potential temporal bias, as Douyin\u0026rsquo;s recommendation algorithms, policies on senior creators, and audience behaviors are subject to rapid change. Longitudinal research would help address this limitation and strengthen the robustness of conclusions. Additionally, while this study integrates quantitative content analysis, interface walkthroughs, and qualitative interviews, it remains exploratory in scope. Expanding to larger-scale mixed-methods approaches and computational techniques, including large-sample algorithm audits or social network analysis, could more systematically evaluate the structural embedding of algorithmic bias. Future studies should also situate intersectional discrimination within different cultural and policy contexts, recognizing that algorithmic systems operate within sociotechnical environments deeply shaped by history and culture (Bowker \u0026amp; Star, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Noble, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the \u0026quot;Ethical Review Measures for Life Sciences and Medical Research Involving Humans\u0026quot; issued by the Central People\u0026apos;s Government of the People\u0026apos;s Republic of China (Document No. GWKJFA [2023] No. 4), ethical review work must be conducted for research involving human life sciences and medicine. For humanities and social sciences research (excluding neurophysiological experiments), centralized approval from an Institutional Review Board (ethics review committee) is not mandatory, but internal ethical oversight is required.\u003c/p\u003e\n\u003cp\u003eTo ensure the ethical standards and rigor of this study, the Ethics Committee of the School of Law and Humanities, Zhejiang Sci-Tech University, conducted a formal review and approved this research on January 15, 2024. The approved study, titled \u0026quot;From being seen to being coded: Technological practices and intergenerational interactions of older short video creators.\u0026quot; This research involved in-depth interviews with adult participants and did not include any biomedical experimentation.\u003c/p\u003e\n\u003cp\u003eAll participants were fully informed about the study\u0026rsquo;s objectives, procedures, and data management protocols. Written informed consent was obtained from every participant between February and October 2024, and all participants were explicitly informed of their right to withdraw from the study at any time without penalty. All personal information and identifiers were removed from the data to ensure participant anonymity and confidentiality..\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of the Declaration of Helsinki (2013 revision), ensuring respect, fairness, and protection of participants\u0026rsquo; rights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Social Science Fund of China (Grant No. 24CXW037); Zhejiang Provincial Education Science Planning Project (Grant No. 2023SCG338); Research Project of Zhejiang Federation of Humanities and Social Sciences (Grant No. 2025N113).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBivens, R. (2017). 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New Labor Forum, 28\u003c/em\u003e(1), 10-29. https://doi.org/10.1177/1095796018819461\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"short video production, ageing, intersectionality, platform algorithms, intergenerational negotiation","lastPublishedDoi":"10.21203/rs.3.rs-7135542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7135542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the intersectional dynamics of age, gender, and algorithmic power within the short video production practices of older adults on China's Douyin platform and its affiliated editing tool, CapCut. Using a mixed-methods approach, the research conceptualizes these platforms as a techno-field to explore how older users navigate digital content creation. Findings reveal a pronounced gendered division in technical engagement: older male users tend to adopt advanced editing features to reinforce domestic authority, while older female users predominantly rely on emotionally themed templates to maintain intergenerational bonds. The Douyin algorithm subtly deprioritizes content tagged as \"older adults,\" with female creators disproportionately affected by reduced visibility. Furthermore, algorithms reconfigure intergenerational negotiations, adult children assert authority through technological metrics, while older parents adopt conciliatory strategies to preserve familial intimacy. 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