Bibliographic record
Abstract
To date, there has been little empirical research on how food studies pedagogy has developed in Canada. Yet, across Canada, more and more postsecondary institutions are offering food studies in formalized programs and individual courses to undergraduate students. This paper contributes to the literature on food studies pedagogy by gathering insights from interviews with key faculty in food studies undergraduate programs at Canadian higher education institutions, and other food studies scholars in Canada. The purpose of this empirical research is to provide clarity regarding the ways that food studies programs are conceptualized and taught to better understand the evolution and future course of food studies pedagogy. Semi-structured interviews were undertaken to explore the normative commitments and philosophical underpinnings of food studies programs; various ways that scholars scope food studies; and challenges faced by food studies programs. We found that food studies programs in higher education in Canada and their associated pedagogy do not have a set of fixed attributes, but they do share common threads. Transformation is a defining characteristic of food studies and its pedagogy and puts critical thinking at the core of how food studies are taught in Canada at the undergraduate level. Interviewees also emphasized the importance of moving beyond critique towards solutions in their teaching to facilitate a transition towards more socially and ecologically just 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".