City Region Food Systems: Building Resilience to COVID-19 and Other Shocks
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
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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