A CASE STUDY APPROACH TO FOOD SOVEREIGNTY AND UNIVERSAL, CURRICULUM INTEGRATED SCHOOL LUNCH PROGRAMS IN CANADA
Bibliographic record
Abstract
In 2021, a school lunch pilot project, Good Food for Learning, was launched to test two elements of comprehensive and holistic school food programs (SFPs), universality and curriculum-integration, in two low-income, culturally diverse elementary schools in Saskatoon. Drawing from case study research including findings from student focus groups, a process evaluation over the two-year period, and interviews with school staff on the benefits and challenges of the pilot, this dissertation aimed to understand how universal, curriculum-integrated school lunch programs contributed towards food sovereignty in Canada. Food sovereignty is defined as “the right of peoples to healthy and culturally appropriate food produced through ecologically sound and sustainable methods, and their right to define their own food and agriculture systems” (Declaration of Nyéléni, 2007, p.13). Food sovereignty advocates for social transformation of food systems and relationships between people and the environment. Results from the pilot case study demonstrated that universal, curriculum-integrated SFPs can contribute to the food sovereignty tenets of focusing on food for people, valuing food providers, localizing food systems, and establishing local control. However, challenges included pilot complexity, dedicated SFP funding, access to knowledge and information, and training for workers, ultimately limited the embeddedness of the pilot in the schools. Alongside program implementation, time for critical reflection and food systems learning with school staff from a lens of food justice and trauma-informed nutrition is needed to refresh attitudes and knowledge to align with the social innovation of universal and curriculum-integrated programming. The foundation of school food development in Canada’s future should include community-led, food systems approaches and attention and funding to eliminate gendered assumptions about school food work. Future research to dispel the imagined separation between education/health systems, understand and document the role of care work, and improve food-centered relationships between students and staff is needed to strengthen SFPs and support food sovereignty in Canada.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.034 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".