Assessing Coordination of Legal-Based Efforts across Jurisdictions and Sectors for Obesity Prevention and Control
Bibliographic record
Abstract
America’s increasing obesity problem requires federal, state, and local lawyers, policymakers, and public health practitioners to consider legal strategies to encourage healthy eating and physical activity. The complexity of the legal landscape as it affects obesity requires an analysis of coordination across multiple sectors and disciplines. Government jurisdictions can be viewed “vertically,” including the local, state, tribal, and federal levels, or “horizontally” as agencies or branches of government at the same vertical level. Inspired by the successful tobacco control movement, obesity prevention advocates seek comprehensive strategies to “normalize” healthy behaviors by creating environmental and legal changes that ensure healthy choices are the default or easy choices. With many competing demands on diminishing municipal budgets, strategic coordination both vertically and horizontally is essential to foster the environmental and social changes needed to reverse the obesity epidemic.
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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 | 0.110 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".