Bibliographic record
Abstract
At the beginning of the COVID-19 Pandemic, many warned that the resilience of the global, industrial food system would be tested. We conducted regular interviews in 2020 with key actors at the Ontario Food Terminal, North America’s third largest produce wholesale market, to better understand urban food system resilience in the first year of the Pandemic. How major wholesale marketplaces, such as the Ontario Food Terminal, fare during emergencies is key to understanding urban food system resilience, as these institutions connect farms to cities. Widescale interruptions to the supply of fresh produce did not take place at the Terminal despite challenges. We present data from the frontlines, documenting the challenges participants faced and their adaptive capacity. We find that food system resilience was rooted in pre-existing relationships, the adaptability of actors in produce supply chains, and worker stress and effort. We caution that, even though the system displayed resiliency, this does not mean that it is inherently resilient. We highlight vulnerabilities in the status quo and raise a red flag around the future ability of the system to withstand shocks. We conclude that, because the system resilience we document depends on people, the well-being of humans in the system is key to resilience of the food system itself.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".