The Socio-Cultural Benefits of Urban Agriculture: A Review of the Literature
Bibliographic record
Abstract
Despite extensive literature on the socio-cultural services of urban open spaces, the role of food-producing spaces has not received sufficient attention. This hampers advocacy for preserving and growing urban agricultural activities, often dismissed on justifications that their contributions to overall food supply are negligible. To understand how the social benefits of urban agriculture have been measured, we conducted a systematic review of 272 peer-reviewed publications, which drew on insights from urban agriculture sites in 57 different countries. Through content analysis, we investigated socio-cultural benefits in four spheres: engaged and cohesive communities, health and well-being, economic opportunities, and education. The analysis revealed growth in research on the social impacts of gardens and farms, with most studies measuring the effects on community cohesion and engagement, followed by increased availability and consumption of fruits and vegetables associated with reduced food insecurity and better health. Fewer studies assessed the impact of urban farming on educational and economic outcomes. Quantifying the multiple ways in which urban agriculture provides benefits to people will empower planners and the private sector to justify future investments. These findings are also informative for research theorizing cities as socio-ecological systems and broader efforts to measure the benefits of urban agriculture, in its many forms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".