Campus Food Movements and Community Service-Learning: Mobilizing Partnerships through the Good Food Challenge in Canada
Bibliographic record
Abstract
This paper addresses the growing collaborations among students, faculty and community-practitioners attempting to build healthy, equitable and sustainable food systems within post-secondary institutions and the ensuing implications for food movements. Specifically, we investigate the role of Community Service-Learning (CSL) in fostering food systems change through a case study of Planning for Change: Community Development in Action, a graduate CSL course at the University of Toronto and a partnership with Meal Exchange, a national non-profit organization, to develop the Good Food Challenge on college and university campuses across Canada. Using CSL to support social movements is not uncommon; however, there has been little application of these pedagogical approaches within the field of food systems studies, especially in the area of campus food movements that engage diverse groups in mutually beneficial and transformative projects. Our description of the case study is organized into three categories that focus on key sites of theory, practice and reflection: classroom spaces, community spaces and spaces of engagement. Through reflection on these spaces, we demonstrate the potential of CSL to contribute to a more robust sustainable food movement through vibrant academic and community partnerships. Together, these spaces demonstrate how campus-based collaborations can be strategic levers in shifting towards more healthy, sustainable and equitable 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.013 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".