Co-healthism in Health at Every Size®-aligned TikTok activism: Transforming the definition of healthism via feminist-of-color disability studies theorizing
Bibliographic record
Abstract
This article is a feminist-of-color disability studies-oriented TikTok critical discourse analysis of 100 popular Health at Every Size® (HAES®) TikToks. It evaluates the power relations surrounding how HAES® frameworks are disseminated on TikTok and unpacks the implications for fat liberation. HAES® frameworks of care capacitate the eradication of medical fatmisia and the weight-centered health paradigm. While popular among online fat activists, HAES® frameworks have been widely accused of perpetuating healthism. My analysis makes clear how HAES® discourse is often steeped in ableist, white supremacist, and colonial healthism. Through this analysis, I ultimately argue that the definition of healthism needs to shift to center ableism, white supremacy, and colonialism, as well as the co-constituting nature of how systems of oppression aggregate to weaponize health against those with embodiments marked as “unhealthy.” I argue that this shift is necessary to produce analyses committed to radical solidarity. I mark this shift by coining and utilizing the term co-healthism. To demonstrate the need for my argument, I organize the results into three themes: healthist ableism, healthist white supremacy, and healthist colonialism in HAES® discourse. In the discussion, I clarify the co-constitutive nature of these themes and argue for the shift from healthism to co-healthism. I also define the concept and explain its methodological potentialities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".