Historical lessons for Canada’s emerging national school food policy: an opportunity to improve child health
Bibliographic record
Abstract
School meals are one of the most successful drivers of improved health and education. In 2021, the Canadian federal government committed $1 billion over 5 years to develop a national school food policy and work towards a national school nutritious meal program. Canadian policy makers should learn from the experiences of other countries, including the United States’ National School Lunch Program. We propose 3 priority areas to maximize health improvements: (1) resisting corporatization and prioritizing health; (2) preventing stigma through universal access; and (3) ensuring cultural inclusion and appropriateness.Les repas servis à l’école sont l’un des facteurs les plus efficaces contribuant à l’amélioration de la santé et de l’éducation. En 2021, le gouvernement fédéral du Canada a débloqué un milliard de dollars sur cinq ans pour mettre en place une politique nationale en matière d’alimentation dans les écoles et pour élaborer un programme national de repas scolaires. Les décideurs canadiens devraient s’inspirer des expériences d’autres pays, en particulier du Programme de distribution de repas dans les écoles des États-Unis. Nous proposons trois secteurs prioritaires pour maximiser les effets positifs sur la santé : 1) résister à la privatisation et prioriser la santé, 2) prévenir la stigmatisation en assurant l’accessibilité universelle et 3) assurer l’inclusion et la pertinence culturelles.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".