Law as a Tool in “The War on Obesity”: Useful Interventions, Maybe, But, First, What's the Problem?
Bibliographic record
Abstract
This article explores the effectiveness of legal interventions to promote healthier eating/drinking and exercise in responding to obesity. Undue emphasis on weight loss and prevention of excess gain have largely been failures and have fueled prejudice against fat people. A major challenge lies in shifting norms: away from stigmatization of the obese and towards more nutritious eating/drinking and increased activity with acceptance of bodies in all shapes and sizes. Part of the enormity of this challenge lies in the complex effects of law and its relationship with norms, including unintended consequences of regulation. To illustrate such complications, the paper examines two interventions and the actual effects that they have produced (or could under differing conditions): junk food taxes, particularly on SSBs (sugar sweetened beverages), and restriction of advertising to children, especially the ban in Quebec.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".