Legislating Away Illness: Examining Efforts to Curb the Development of Eating Disorders Through Law
Bibliographic record
Abstract
In response to concerns over the alarmingly high incidence of eating disorders, both the Israeli Parliament and the French Parliament have passed legislation restricting advertising and modelling practices, and the California State Assembly has recently sought to do the same. In light of the severity and prevalence of eating disorders, as well as the growing body of evidence suggesting a strong connection between the media and the proliferation of eating disorders, legislators are understandably attempting to stem this scourge through legal means. Although recent attempts to “legislate away” eating disorders are laudable and may make a significant difference in reversing current trends, this article argues that such legal measures are not enough. The prospect of regulating the fashion and media industries through law raises several important questions. To what extent should the state regulate commercial expression to protect vulnerable individuals? Is such an intrusion overly paternalistic? If law is the answer (or at least part of the answer), precisely how should the legislation be devised, and what might be its contours? How do we strike a balance between the public interest in promoting healthy body images and protecting lives and the interests of fashion and media industry stakeholders’ freedom of expression? More generally, a key concern canvassed in this article is how the appropriate balance can be struck between public health concerns and other compelling interests, principles, and values.
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".