Centring Children, Health and Justice at the Core of Canadian School Food Programs
Bibliographic record
Abstract
Canada remains one of very few affluent countries without a national school food program (SFP), and the federal government recently expressed support for developing such a program. In doing so, the government was responding to growing calls for state funding to support a national program coming from the Coalition for Healthy School Food (CHSF). Despite years of multi-stakeholder advocacy for and strong international evidence extolling the benefits of universal SFPs, there remain seemingly intractable debates about for what and whom Canadian SFPs should be designed to serve. To move forward, we propose a clearer articulation and shared understanding of the core goals of a robust Canada-wide SFP. This chapter brings together reflections from the literature and first hand perspectives of people on the front lines of SFP design and implementation with data from a recent case study that draws on the voices of students, parents and staff from a suburban Canadian school district as it transitioned to a new lunch program model. From these insights, we collectively argue that to transcend current deadlocks around designing a future national SFP, Canadian policy makers must actively centre the voices and needs of children, and pursue comprehensive notions of wellbeing and justice at the heart of school food programming.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.052 | 0.033 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".