Bibliographic record
Abstract
This paper argues that common social narratives about unhealthy eaters can cause significant damage to agency. I identify and analyze a narrative that combines a “control model” of eating agency with the healthist assumption that health is the ultimate end of eating. I argue that this narrative produces and enables four types of damage to the agency of those identified as unhealthy eaters. Due to uncertainty about what counts as healthy eating and various forms of prejudice, the unhealthy eater label and its harms to agency are more likely to stick to some people than others and may reinforce patterns of oppression. I argue that fat people are especially vulnerable to this identification and the damage it can do. I then consider possible “counterstories” about unhealthy eaters, alternative narratives that might be less damaging to agency than the control narrative. I identify one promising counterstory but suggest that it may be limited when it comes to repairing damage to the agency of fat people. Overall, this paper illustrates some of the complex ways that healthism about eating affects agency, and emphasizes the ethical importance of the ways we think about and discuss eating and eaters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".