City Region Food Systems: Building Resilience to COVID-19 and Other Shocks
Bibliographic record
Abstract
Using examples from the COVID-19 pandemic, this paper reviews the contribution a City Region Food Systems (CRFS) approach makes to regional sustainability and resilience for existing and future shocks including climate change. We include both explicit interventions under United Nations Food and Agriculture Organization (FAO-RUAF) led initiatives, as well as ad hoc efforts that engage with elements of the CRFS approach. To provide context, we begin with a literature review of the CRFS approach followed by an overview of the global food crisis, where we outline many of the challenges inherent to the industrial capital driven food system. Next, we elaborate three key entry points for the CRFS approach—multistakeholder engagement across urban rural spaces; the infrastructure needed to support more robust CRFS; system centered planning, and, the role of policy in enabling (or thwarting) food system sustainability. The pandemic raises questions and provides insights about how to foster more resilient food systems, and provides lessons for the future for the City Region Food System approach in the context of others shocks including climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".