Assessing and Planning Sustainable City Region Food Systems: Insights from Two Latin American Cities
Bibliographic record
Abstract
In the context of growing urbanisation, urban poverty, and climate change impacts, the importance of urban food security and urban food systems is increasingly recognised by both local and national governments, as well as international actors. There is also a growing understanding that urban development and food systems cannot be decoupled from rural development given the multiple impacts that urban areas have on their surroundings. In recent years the concept of City Region Food Systems (CRFS) has emerged as a promising approach to support local governments, policy makers, and multi-stakeholder bodies in making informed decisions to improve urban and regional food system sustainability and resilience, while taking into account a more integrated approach to territorial development across urban and rural areas. This paper is based on an ongoing FAO and RUAF programme of assessing and planning City Region Food Systems, currently implemented in eight city regions in Canada, Colombia, Ecuador, Senegal, Sri Lanka, The Netherlands, and Zambia. The paper analyses the content, definition and delimitations of the concept of City Region Food Systems by presenting two case studies from Latin America (Quito and Medellín), and discusses first advances in policy uptake and territorial food planning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".