Toward Food System Sustainability through School Food System Change: Think&EatGreen@School and the Making of a Community-University Research Alliance
Bibliographic record
Abstract
This paper describes the theoretical and conceptual framework and the research and practice model of Think&EatGreen@School, a community-based action research project aiming to foster food citizenship in the City of Vancouver and to develop a model of sustainable institutional food systems in public schools. The authors argue that educational and policy interventions at the school and school board level can drive the goals of food system sustainability, food security, and food sovereignty. The complex relationship between food systems, climate change and environmental degradation require that international initiatives promoting sustainability be vigorously complemented by local multi-stakeholder efforts to preserve or restore the capacity to produce food in a durable manner. As a step towards making the City of Vancouver green, we are currently involved in attempts to transform the food system of the local schools by mobilizing the energy of a transdisciplinary research team of twelve university researchers, over 300 undergraduate and graduate students, and twenty community-based researchers and organizations working on food, public health, environmental and sustainability education.
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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 | 0.046 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".