Opportunities and Challenges of Developing a Culinary Food Studies Bachelor’s Degree
Bibliographic record
Abstract
Although Food Studies has been acknowledged as a distinctive field in Canada for almost two decades, until now there has not been an undergraduate degree in Food Studies in this country. This is changing with the development of Canada’s first Honours Bachelor’s Degree in Food Studies (BFS) at [Ontario College], set to launch in September 2021. This field report describes the process, opportunities, and challenges of developing a Food Studies degree at an Ontario college. It explores the unique openings at the intersection of food studies education and applied practical skills training for work in the food sector. In particular, we ask: What can food studies bring to culinary education? And, what can culinary education bring to food studies? We content that food studies can lend to a more transformative culinary education focused on social, cultural, political, and environmental influences in the food system. Simultaneously, culinary education brings distinct insights into operationalization within the food sector which provide new openings for applied research. We demonstrate the need for this new collaboration and knowledge as a necessity of a turbulent world.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".