School Food Operation Models: Program Typologies
Bibliographic record
Abstract
In April 2024, the Government of Canada announced the establishment of a National School Food Program with a funding commitment of one billion dollars over five years. Then, in June 2024, they released a National School Food Policy that will frame the development of the program as it is established. To date, there has been little research examining how programs operate. To develop a nationally-harmonised program consistent with the new National School Food Policy,there is a need for an in-depth understanding of how school food models operate. The purpose of this project was to adapt, detail, and validate preliminary SFP operation models – food procurement, production and service - developed from case studies of promising programs across Canada, see the School Food Programs in Canada – 15 Promising Cases report for more information. The school food operation models – which we collectively refer to as school food typologies - can help inform Canada’s National School Food Program, a comprehensive national research framework for Canada, as well as other country's programs. This report also acts as a supplement to the University of Saskatchewan's School Food How To Guide: Operations and Costing manual.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.008 |
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".