Urban Agriculture Interventions in Refugee and Immigrant Communities: A Scoping Review
Bibliographic record
Abstract
Urban agriculture, known as urban farming, urban gardening, or community gardening, has become an important avenue for community development, food security, and economic stability in response to increased urbanization. However, a less studied aspect of urban agriculture is its application for historically marginalized communities and refugee and immigrant communities specifically. Using a two-fold research question: What are the domains of application of urban agriculture interventions on refugee and/or migrant populations? What are the scales and geographic patterns of urban agriculture interventions? Following scoping review guidelines, 42 articles published from 1990 to 2024 were included after screening out 375 articles that were initially retrieved from the database search. Articles were examined based on the following criterion: population of interest, intervention type, intervention scale, and geography of author. Findings suggest five domains of application: well-being, physical health, ecological, economic, and sociological, the latter as the most common domain. Health, particularly mental health, was less evident in scholarship. In terms of scale and geography, findings suggest that studies about large-size interventions were mostly in the Global South (Middle East and African regions specifically), and studies on small and medium-sized interventions were in the Global North (United States, Canada and Australia specifically). For theory, findings point to two broad theoretical domains: relationality and materialist, and less attention to food and environmental justice. These findings raise questions pertaining to access to resources insofar as resources determine the scale/size of interventions and thus their application. Issues pertaining to health and food and environmental justice were applications that largely did not emerge in the data, raising questions for further research.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".