(Re)claiming subversive spaces on TikTok: the complexities of body activisms within physical activity cultures
Bibliographic record
Abstract
Feminist organising in digital spaces, such as social media, has the potential for activism, resistance, and visibility; yet these spaces continue to be dominated by narrow body representations and shaped by unequal power dynamics. We propose that bodies of difference may find vibrant and difference-affirming homes within their digital communities via innovative algorithm-based social media spheres, such as TikTok. Using a creative methodological mashup that we call a ‘collaborative digital autoethnography’, we explored our experiences of difference-affirming hashtags (e.g. #CurvyGym, #DisabledFitness, #QueerFitness) and creators who shared explicitly inclusive and/or subversive content (e.g. #ThickTocker). Using reflexive thematic analysis and thinking with/through Safiya Noble’s work on algorithms of oppression, we developed four themes: #TheFringe, #(Re)claimingFlesh, #CircleOfSurveillance, and #TheWrongSideOfTikTok. In #TheFringe, we found normatively embodied creators who used TikTok as a space to question normative fitness trends – content that helped build our difference-affirming algorithmic space. In #(Re)claimingFlesh, fat-positive creators used TikTok to unapologetically reveal or grab body fat/flesh, and TikTokkers re-storied their body-related journeys by flipping the script of harmful body surveillance (i.e. #CircleOfSurveillance). Lastly, some creators shared grievances about the (oppressive) algorithms of TikTok that banished them to #TheWrongSideOfTikTok. We provide reflexive accounts of our own embodied differences while exploring the TikTok platform, searching for spaces that affirm our bodies while contending with algorithmic oppression. We conclude by discussing TikTok as a platform where powerful collective activism and revolution can form, yet always in contention with trolling, hate, and harmful algorithms that constrain activist movements within sport, exercise, and health contexts.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".