From Leader to Laggard: Reflections on Food Provisioning at the University of Toronto during the COVID-19 Pandemic
Bibliographic record
Abstract
This paper investigates the significant shifts in food provisioning at the University of Toronto during COVID-19 and throughout subsequent years. The institutional response to the pandemic challenged, and in some cases, dismantled the fundamental undergirding of progressive food policies which have long been celebrated and promoted by university administrators. Campus dining halls are important: they directly influence the mental and physical health of millions of students each year throughout Canada, impart dietary practices which shape health outcomes in future, and generate significant environmental impacts. Changes to these dining halls have been executed through a hasty reordering and reorganization of the ideological, material, and spatial dimensions of food across campus. Data collection methods include ethnographic observations, documents accessed through FIPPA requests, digital media and university communications, internal and external reports, and meeting minutes. My findings contest the notion of ‘in-house’ food provisioning as a more progressive, sustainable, and ethical model. Further, they illustrate the potential for public institutions to implement foodservice models that increasingly converge with those of multinational corporations. These new dining hall spaces feature a growing encroachment of corporations into campus spaces and represent a shift in which students are remade into neoliberal consumers. Correspondingly, these changes appear to have negative outcomes on issues ranging from food security, employee precarity, affordability, sustainability, and nutrition. As a food activist and scholar directly impacted by these transitions, I reflect on multiple years of working in residence, discussing modes of resistance, contestations, and institutional responses throughout my tenure.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.054 | 0.025 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".