School Food Case Study: Canada
Bibliographic record
Abstract
This school meals case study forms part of a collection led by the Research Consortium for School Health and Nutrition’s "Good Examples" Community of Practice. Developed by a sub-group of academic members of the Canadian Association for Food Studies' School Food Working Group and validated by Canadian Coalition for Healthy School Food, the School Meals Case Study of Canada serves to document how the school meals programme is organized, funded, and monitored throughout the country. The objectives of this case study include presenting an introduction to the country profile, outlining the design and implementation of school feeding programmes, describing their monitoring and evaluation processes, and highlighting lessons learned, best practices, and challenges. This case study is written as a working paper, and can be updated to reflect evolving circumstances. The ‘Good Examples’ Community of Practice supports the evidence generation of the Research Consortium for School Health and Nutrition, the evidence-generating arm of the School Meals Coalition. The Research Consortium’s objective is to carry out independent research across diverse sectors and generate solid, compelling, and actionable evidence regarding the benefits of school food programs to inform evidence-based decision-making on school health and nutrition policies and practices.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.019 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".