The Intersection of Planning, Urban Agriculture, and Food Justice: A Review of the Literature
Bibliographic record
Abstract
Abstract Problem, research strategy, and findings: We draw on a multidisciplinary body of research to consider how planning for urban agriculture can foster food justice by benefitting socioeconomically disadvantaged residents. The potential social benefits of urban agriculture include increased access to food, positive health impacts, skill building, community development, and connections to broader social change efforts. The literature suggests, however, caution in automatically conflating urban agriculture’s social benefits with the goals of food justice. Urban agriculture may reinforce and deepen societal inequities by benefitting better resourced organizations and the propertied class and contributing to the displacement of lower-income households. The precarious- ness of land access for urban agriculture is another limitation, particularly for disadvantaged communities. Planners have recently begun to pay increased attention to urban agriculture but should more explicitly sup- port the goals of food justice in their urban agriculture policies and programs. Takeaway for practice: We suggest several key strategies for planners to more explicitly orient their urban agriculture efforts to support food justice, including prioritizing urban agriculture in long-term planning efforts, developing mutually respectful relationships with food justice organizations and urban agriculture participants from diverse backgrounds, targeting city investments in urban agriculture to benefit historically disadvantaged communities, increasing the amount of land permanently available for urban agriculture, and confronting the threats of gentrification and displacement from urban agriculture. We demonstrate how the city of Seattle (WA) used an equity lens in all of its programs to shift its urban agriculture planning to more explicitly foster food justice, providing clear examples for other cities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".