The Ethical Defensibility of Harm Reduction and Eating Disorders
Bibliographic record
Abstract
Eating disorders are mental illnesses that can have a significant and persistent physical impact, especially for those who are not treated early in their disease trajectory. Although many persons with eating disorders may make a full recovery, some may not; this is especially the case when it comes to persons with severe and enduring anorexia nervosa (SEAN), namely, those who have had anorexia for between 6 and 12 years or more. Given that persons with SEAN are less likely to make a full recovery, a different treatment philosophy might be ethically warranted. One potential yet scarcely considered way to treat persons with SEAN is that of a harm reduction approach. A harm reduction philosophy is deemed widely defensible in certain contexts (e.g. in the substance use and addictions domain), and in this paper we argue that it may be similarly ethically defensible for treating persons with SEAN in some circumstances.
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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 | 0.075 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.100 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 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".