Planning the foodshed: Rural and peri‐urban factors in local food strategies of major cities in Canada and the United States
Bibliographic record
Abstract
Abstract Many North American cities seek to increase access to locally sourced foods. But to what degree are cities planning for and supporting peri‐urban and rural food production? We examined this question by analyzing 25 documents from 22 major United States and Canadian cities (>300,000 residents) with local food strategies or action plans, focusing on social, ecological, and technological factors relevant to local food production. Our review suggests that the following topics are often overlooked: farmland access and quality, farm viability, agricultural training and workforce, environmental and public health, processing infrastructure, climate change adaptation, and particular needs pertinent to those concerns among marginalized groups, including Black, Indigenous, and People of Color. Just under half of the cities defined how success or progress toward reaching their food‐related goals would be monitored and measured. Many municipalities considered collaboration as an important governance tool for realizing their strategies toward local food systems, including with actors from within the city and beyond, as cities’ governance scope, resources, and power are often limited. Besides illustrating ongoing municipal efforts to enhance local food systems, our study identifies focus areas in food policy and planning to avoid overlooking social and environmental trade‐offs in local foodsheds, including potentially overestimating self‐sufficiency capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".