How cities source their food: spatial interactions in West African urban food supply
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
Abstract In West Africa, increasing rural–urban flows of food, driven by growing urban populations, require functional, efficient links between cities and production areas. However, underlying mechanisms of urban food sourcing in West Africa are poorly understood. This study deepens understanding of spatial interactions between cities and production areas by examining the effects of settlement size, geographical distance, and agricultural suitability on food inflows to four West African cities. The analysis was informed by theoretical spatial models and data on food flows, road network, agricultural suitability, and settlements. Results showed that food travelled further from larger supplying settlements, and towards the two larger destination cities. This supports the idea of a hierarchical system, where food provisioning area and upstream supply chain length increase with settlement size. Overall, towns with fewer than 100,000 inhabitants, often representing aggregation centres, were among the major suppliers to the cities. Complementary agricultural suitability between origin and destination shaped food flow direction and length, but poor road access and international borders impeded trade. Spatial models did not fully explain food flows: they were also influenced by historical factors shaping certain settlements’ importance as sources. Study cities were supplied by a diversity of more and less concentrated food sources, representing production sites or aggregating markets, which should theoretically support food supply resilience. Improvements to storage and road infrastructure, and removing trade barriers, could improve food supply to cities, and producer and trader livelihoods. Emerging research on urban food systems governance could support understanding of how best to do this.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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