Critical food systems education in university student-run food initiatives: learning opportunities for food systems transformation
Bibliographic record
Abstract
Introduction Student-run Campus Food Systems Alternatives (CFSA) have been proposed as spaces which have the potential to advance Critical Food Systems Education (CFSE) – the objective of which is to motivate students to act toward radical food systems transformation on community and systemic levels. Evidence on how learning dynamics in CFSA drive student participants to develop critical perspectives on food systems is limited, however. This paper seeks to address this gap by exploring how critical and transformative learning happens in these informal and student-run spaces, by detailing a multi-case study of students’ learning experiences in four student-run CFSA on the McGill University campus. Methods Data on students’ learning experiences was collected through observational field notes of CFSA activities and semi-structured Interviews with student facilitators. Thematic and cross-case analysis was performed with interview data. Results Analysis of students’ described learning experiences in CFSA revealed three broad categories of learning dynamics which drive students’ learning about food systems and their willingness to act for food systems change: hands-on work in informal spaces, social connection and engagement between student participants, and engagement with the beyond-campus community. Discussion Engagement with the beyond-campus community via CFSA, particularly that which involved exposure to food-related injustice in marginalized communities, was found to be particularly important in driving student participants’ critical reflection on food systems and willingness to act toward food justice. A lack of intentional critical reflective practice was however observed in CFSA, calling into question how this practice can be driven in campus food initiatives without compromising their student-run and informal structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".