Insights from the Think&EatGreen@School Project: How a community-based action research project contributed to healthy and sustainable school food systems in Vancouver
Bibliographic record
Abstract
From 2010 to 2016 the Think&EatGreen@School project worked to create healthy and sustainable school food systems in the Vancouver School Board. Using models of Community-Engaged Scholarship and Community-Based Action Research, we implemented diverse programmatic and monitoring activities to provide students and teachers with hands-on food cycle education, in order to influence policy, and to encourage university students to engage actively with the food system. Our focus was on transformation of local school food systems as a context-specific means to address serious global issues related to food security, health and environmental sustainability. This paper provides a synthesis of the project including the context that led to its inception, its overarching goals, methodological framework and areas of impact. Key learnings from this project highlight the need for continued work to integrate research, teaching and action on global food security, environmental and public health challenges and to build connections to create healthy, sustainable school food systems.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".