Food systems during the COVID-19 pandemic: vulnerabilities, adaptations, and resilience
Bibliographic record
Abstract
Abstract This paper reviews the emerging literature on food systems and food supply chains during the COVID-19 pandemic. Four themes are explored: consumer demand and retail market effects; supply-side shocks; food system and supply chain resilience; and developing countries and food insecurity. The effect of demand-side shocks is explored, including the sudden shift in expenditures from food service to food retail. Longer-run structural changes in the food retailing landscape include the expansion of online food delivery. The effect of supply-side shocks is examined extensively in the literature, including short-run adaptations as supply chains pivoted from the food service sector to food retail, along with supply-side disruptions due to labour force outbreaks of COVID-19. Resilience is a common theme in the literature, at both food system and food supply chain levels. While a variety of perspectives are offered, most assessments point to a surprising degree of resilience and adaptability, while identifying the points of vulnerability. The pandemic increased food insecurity through the effect on household incomes from reduced labour mobility, lockdowns, and a contraction in economic activity. These effects were particularly prominent among vulnerable populations in developing countries. Significant attention has been paid to the short- and medium-run effects of the pandemic on food systems, with further research needed to understand any longer-term structural changes that may arise. The COVID-19 pandemic offers lessons for the robustness of food systems and the importance of timely, well-informed policy responses in preparation for future global shocks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".