A systems approach to navigating food security during COVID-19: Gaps, opportunities, and policy supports
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
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.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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