Legal interventions to address obesity: assessing the state of the law in Canada
Bibliographic record
Abstract
Over the past decade, public health experts have raised the alarm over the expanding number of people in countries around the world who are overweight and obese. Canada is no exception: approximately 60 per cent of Canadian adults and 35 per cent of children are overweight or obese. Obesity, in particular, is associated with higher rates of diabetes, hypertension, cardiovascular disease, and some cancers. The medical, economic, and social consequences of these rates of overweight and obesity, especially among children, have caught the attention of our governments. In the last several years, various provincial and federal committees have produced detailed reports on obesity, which can be stacked alongside similar reports issued by ocher countries, as well as those from international bodies such as the World Health Organization. These reports are uniform in their calls for coordinated and comprehensive measures-including legal interventions-to promote healthier diets and more physical activity. Although a growing body of literature explores law as a tool to control factors associated with obesity, existing analyses focus predominantly on the legal and cultural context of the United States.' Our objective in this article is to provide a systematic analysis of the use of legal interventions to address obesity in Canada.
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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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".