A global food systems framework for pandemic prevention, response, and recovery
Bibliographic record
Abstract
COVID-19 has highlighted the dynamic relationship between pandemic threats and global food systems. Despite important connections, research and policy-making on food systems and pandemics largely operate in silos. We propose a framework that integrates food systems and pandemic planning and response, exploring the role of the food system in shaping pandemics and, consequently, the role of pandemics in disrupting a now global food system. This framework highlights important connections between food production, distribution, and consumption at each stage of the pandemic cycle: prevention, response, and recovery. We use recent experiences with COVID-19 to illustrate vulnerabilities in systems interaction during the prevention and response phases. Over the long term, in the recovery phase, food systems must transform, adopting an enhanced level of functioning to improve resilience. To reduce population health risks and promote sustainable food systems, we call for implementation of surveillance systems for both emerging infections and food systems functioning in order to strengthen global food supply chains, create stakeholder resource coordination mechanisms, and address underlying socioeconomic vulnerabilities. Multidisciplinary global actors should draw on lessons from the COVID-19 pandemic to prevent the inevitable next one.
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.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".