A systems approach to navigating food security during COVID-19: Gaps, opportunities, and policy supports
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted a series of concatenating problems in the global production and distribution of food. Trade barriers, seasonal labor shortages, food loss and waste, and food safety concerns combine to engender vulnerabilities in food systems. A variety of actors—from academics to policy-makers, community organizers, farmers, and homesteaders—are considering the undertaking of creating more resilient food systems. Conventional approaches include fine-tuning existing value chains, consolidating national food distribution systems and bolstering inventory and storage. This paper highlights three alternative strategies for securing a more resilient food system, namely: (i.) leveraging underutilized, often urban, spaces for food production; (ii.) rethinking food waste as a resource; and (iii.) constructing production-distribution-waste networks, as opposed to chains. Various food systems actors have pursued these strategies for decades. Yet, we argue that the COVID-19 pandemic forces us to urgently consider such novel assemblages of actors, institutions, and technologies as key levers in achieving longer term food system resilience. These strategies are often centered around principles of redistribution and reciprocity, and focus on smaller scales, from individual households to communities. We highlight examples that have emerged in the spring-summer of 2020 of household and community efforts to reconstruct a more resilient food system. We also undertake a policy analysis to sketch how government supports can facilitate the emergence of these efforts and mobilization beyond the immediate confines of the pandemic.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".