Bibliographic record
Abstract
Food studies is an emerging and interdisciplinary field that has produced abundant theoretical, analytical, and conceptual insights into contemporary agro-food system dynamics. However, space still exists for the convergence of classroom-based food pedagogy and transformative community work to promote social justice frameworks. While calling for a paradigm shift within educational systems, we ask, how can community-based experiential engagement in post-secondary food pedagogy enhance student learning, bridge academic-public divides, and foster transformative social change? Drawing from our experiences farming in Calgary, we argue that activist food studies employed with a learner-centered, place-based teaching approach centering Indigenous Knowledge Systems can support local food networks and build community within and beyond academia. We present strategies for bridging the academic-public divide through a participatory approach and activist scholarship that directly engages with sustainable urban and agrarian development. Complementing course-based theory and literature with applied methodologies that build the technical and leadership capacity of students will enhance student learning, build stronger community ties, and produce meaningful work that connects the local to the global. Furthermore, we will reflect upon our approach, identify potential benefits to students who engage in food studies, and offer recommendations for best practices in food pedagogy that will support social change.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".