"Nothing Makes Me Hate Myself More Than a Skinny White Person on Tumblr": Evaluating Exclusionary Ideals and Racial Discimination in Online Pro-Eating-Disorder Communities
Bibliographic record
Abstract
Pro-eating-disorder internet communities extend hegemonic standards of health, beauty, and fatness to inform their cultural ideals, producing a racialized subculture in which marginalized communities are stereotyped and excluded. I present qualitative content and discourse analyses of pro-ED communities on Tumblr, TikTok, X, and Facebook to examine how they reproduce the domination of racialized bodies, providing serious health risks to marginalized users. BIPOC are predominantly excluded from pro-ED communities, lacking representation or being displayed in explicitly discriminatory presentations. The primary pro-ED ideals are: whiteness, youthfulness, sickness, and emaciation. These exclusionary ideals, reinforcing class distinctions, are also evidenced by the production of idealized subject positions that are informed by racial hierarchies: The Girl Who Has It All, The Beautiful Bag-of-Bones, The Phenom, and The Little Doll. Racialized users are found to internalize the thin white ideal and are enmeshed in moral health discourses which situate them as non-ideal biocitizens, reinforcing structural oppression.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".