Reimagining recipes for food studies:
Bibliographic record
Abstract
This perspective is a continuation of a conversation started during “Reimagining Food, Food Systems, and Food Studies,” a plenary session in which we, the authors, participated at the eighteenth annual assembly of the Canadian Association for Food Studies (CAFS). Assessing current opportunities and limitations for food studies in Canada from our perspectives as emerging scholars, the CAFS panel presented our individual and collective proposals for evolving the field. This article builds on the resonances and dissonances from our discussion to craft a provisional “recipe” for reimagining food studies. Recognizing the shortcomings of the format in terms of its prescriptive connotations, we position recipes not as rigid guidelines for achieving predefined outcomes, but as creative models for generating improvisations. We begin with an overview of the ingredients that have come together to create food studies in Canada. Next, we offer some revisions in the margins of this recipe based on the work in which we are engaged as food scholars and practitioners. Finally, we consider next steps for the work of evolving the field, and we invite readers to share in this exchange. Overall, we observe and participate in an unfinished trajectory that extends from previous questions on why food studies should exist and what food studies is, to consider more deeply how food studies could be done.
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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 | 0.048 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.045 | 0.102 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".