School Meals Case Study: Canada
Bibliographic record
Abstract
School Food Programs (SFPs) in Canada provide an example of how a country without a national school food policy/strategy and program endeavours to nourish students. Globally, Canada ranks 30th out of 38 of the wealthiest nations on child well-being for providing children with access to nutritious food (UNICEF, 2020). Nationally, the diet quality of students across the socio-economic spectrum is poor, with only a small proportion meeting the recommendations of Canada’s Food Guide (Black & Billette, 2013; Everitt et al., 2020; Minaker & Hammond, 2016; Tugault-Lafleur et al., 2017; Tugault-Lafleur et al., 2018; Slater et al., 2022). Canada is the only G7 country (Bas, 2019) and one of the only industrialized member countries of the Organization for Economic Cooperation and Development (OECD) (Koc & Bas, 2012) without a nationally funded and harmonized school food program or policy. Instead, municipal and provincial/territorial (P/T) governments, a few federal government departments/agencies, and non-governmental organizations (NGOs) at all levels support an inconsistent patchwork of programs across Canada (Godin et al., 2017; Haines & Ruetz, 2020; Ruetz & McKenna, 2021). Without a federal school food program for Canada, a grassroots movement of SFPs has developed innovative programs to fill a significant gap. While the majority of students bring a packed lunch, free breakfast and snack programs, and lunch programs have expanded in recent years. This is encouraging, as home-packed school lunches have been found to be low in nutritional quality compared to school-provided meals (Everitt et al., 2020). In 2018/19, at least 1 million, or ⅕, of students participated in a SFP in Canada (an underestimation due to no or limited data in some jurisdictions), but participation rates within provinces and territories varied widely, resulting in inequitable access (Ruetz & McKenna, 2021). The Canadian case is a testament to the leadership and perseverance of (mainly) women, past and present, who recognize the many benefits of universal access to healthy school food and have volunteered countless hours to prepare school meals for students.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.018 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".