Ethical Agreement and Disagreement About Obesity Prevention Policy in the United States
Bibliographic record
Abstract
An active area of public health policy in the United States is policy meant to promote healthy eating, reduce overconsumption of food, and prevent overweight/obesity. Public discussion of such obesity prevention policies includes intense ethical disagreement. We suggest that some ethical disagreements about obesity prevention policies can be seen as rooted in a common concern with equality or with autonomy, but there are disagreements about which dimensions of equality or autonomy have priority, and about whether it is justifiable for policies to diminish equality or autonomy along one dimension in order to increase it along another dimension. We illustrate this point by discussing ethical disagreements about two obesity prevention policies.
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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 | 0.161 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".