DEAR BABY GAYS: INVESTIGATING THE SOCIOTECHNICAL PRACTICES OF OLDER LGBTQ+ TIKTOK USERS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Much scholarship and public discourse alike focus on TikTok’s widespread uptake by young people, including LGBTQ+ youth. However, LGBTQ+ people on the platform often experience challenges relating to visibility and censorship. As users of a variety of ages have joined TikTok’s youthful population, this paper explores the sociotechnical practices of older LGBTQ+ TikTok users as they emerge from, and are shaped by, the platform and its user cultures. It does so through an analysis of older LGBTQ+ TikTokers’ videos and metadata, gathered through novel methods for configuring research accounts to serve up this content to the For You page. Once the accounts were trained to deliver this content through TikTok’s personalized algorithmic curation, videos were collected for one hour per day over a duration of approximately 4 weeks for each account. Preliminary visual and textual analysis of videos indicates recurrent themes related to constructing identities that intersect age with sexual identity, giving advice, sharing about personal experiences and queer history, and circulating counter-discourses against homophobia and transphobia as well as messages of solidarity with targets of discrimination. Analysis of how these users negotiate TikTok’s affordances also indicates that platform’s features, policies, and dominant user practices permeate and shape older LGBTQ+ TikTokers’ self-representations, such that the platform and modes of paying attention to it have become a central element of their content.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it