Canadian School Food Programs and the Prospect of Linking Farms and Schools in Regional Agri-Food Value Chains
Bibliographic record
Abstract
This dissertation investigates the state of, and future prospects for, school food programming in Canada. The aim of this dissertation is to unpack the institutional complexity of school food programs in Canada and assess the operational aspects and economic opportunity of bringing locally grown food or the ‘farm-to-school’. This mixed methods research combines theoretical reflection and empirical approaches to assess prospects for linking local food to children’s health and education as well as the necessary supports for scaling a farm-to-school approach to school food procurement across Canada to benefit students, families, and the Canadian agri-food sector. This dissertation was informed by conceptual and analytical frameworks from economic sociology, local food systems, social movements, social policy, and program development literature. The results provide both a broad overview of the diversity and unevenness of school food programming across Canada through a systematic survey of provincial and territorial government program funders and an in-depth examination of farm-to-school programming in the Province of Ontario through an economic contribution analysis of intermediated local school food procurement followed by a qualitative analysis farm-to-school supply chain actors’ experiences participating in values-based supply chains. For example, it was found that most school food programs (SFPs) in Canada are breakfast programs in elementary schools coordinated by female volunteers; schools heavily rely on donations from community groups, charities, and private sector donors as provincial and territorial governments most often only offer modest funding and formal local food procurement objectives and activities are uncommon but growing in prevalence. This dissertation contributes to scholarship on school food program reach and implementation that has not been systematically examined since the 1990s in Canada, as well as conceptual and practical implications associated with program expansion, governance, and opportunities in the realms of public food procurement and economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".