Food supply chain resilience and the COVID‐19 pandemic: What have we learned?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A year into the COVID‐19 pandemic, this paper reflects on the changes that occurred in agrifood supply chains in Canada and the United States. The sudden shift in food consumption patterns from food service to food retail required realignment of food supply chains. For the most part, food supply chains have performed remarkably well during the pandemic. Cross‐border food supply chains have continued to function effectively. The most significant disruptions emerged from workforce outbreaks of COVID‐19 in the meat processing sector and in fruit and vegetable production. The paper discusses supply chain resilience and argues that agrifood supply chains are characterized by several important differences that need to be taken into consideration when evaluating resilience. Economies of scale and scope offer economic efficiency advantages in normal times, while investments in adaptability and flexibility can enhance resilience for abnormal times. Potential long‐run changes within supply chains include increased automation and digitalization in food supply chains, while investments in infrastructure for online delivery services have permanently altered the food retailing landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it