Fat Liberation or Co-healthist Cooptation? Exploring the Liberatory Potential of the Health at Every Size Framework
Bibliographic record
Abstract
This thesis interrogates the liberatory potential of Health at Every Size (HAES), a popular framework of care within fat activism.It is a feminist-of-colour disability studiesoriented critical discourse analysis of 100 popular TikToks.The analysis demonstrates that HAES meets some peoples' urgent needs for care and healing from fatmisia, particularly those who identify with disordered eating and movement.At the same time, the blurry aggregation of health enhancement and social justice in HAES discourse allows health to be weaponized to justify eugenic projects aimed at proliferating perfectible and standardized ('healthy') bodies and eliminating bodies marked as deviant or 'unhealthy.'The thesis concludes that HAES needs to decentre 'health' and instead focus on care to cultivate a fat liberationist and disability justice aligned politic of care and healing as world-building that dismantles health as a prerequisite for worth.A new framework called Care at Every Size (CAES) is proposed.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".