Serving up food studies online: teaching about “food from somewhere” from nowhere
Bibliographic record
Abstract
Over the past decade, the popularity of food scholarship has led to an expansion of online food studies courses and programs. This push for online course offerings has been escalated due to the COVID-19 pandemic in early 2020. To date, much of the field has focused on examining the global concentration and integration of corporate food systems, often described as a “food from nowhere” regime. In contrast, the study of civil society organizations and social movements working toward more equitable and sustainable food systems points to the desire to (re)build a “food from somewhere” regime. How do these ideas of de-spatialization and re-spatialization apply to teaching online food studies courses? In this reflective essay, five scholars and postsecondary instructors share experiences with online teaching about food systems. Our collective reflection reveals a number of benefits for postsecondary institutions, instructors, students, and pedagogical approaches. We also share key concerns, such as engaging students and encouraging participation, constraints for developing personal connections and the additional time and energy required to prepare and deliver courses. Beyond these opportunities and tensions, we point to the need for instructors to consider the implications of teaching about “food from somewhere” from nowhere. We offer these reflections to begin a much-needed conversation about the current state and the future of online food studies education.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".