Barriers and Facilitators to Engagement in Collective Gardening Among Black African Immigrants in Alberta, Canada
Bibliographic record
Abstract
Background: Community gardens are increasingly popular in Canadian cities, serving as transformative spaces where immigrants can develop self-reliant strategies for accessing culturally familiar and healthy nutritious foods. Past research has demonstrated the embodied health and wellbeing benefits of gardening, however, Black immigrants, reported to be at higher risk of food insecurity are experiencing complex barriers to engagement in collective community gardens. Using a socio-ecological framework, this research explores barriers and facilitators to engagement of Black African immigrants in Alberta, Canada in collective community gardening. Methods: The study adopted a community-based participatory research (CBPR) approach using mixed-methods to explore the individual and collective experiences, challenges, and meanings adopted by immigrants in connection to collective community gardens. Data collection included structured surveys (n=119) to assess general engagement, facilitators, and barriers, in-depth interviews (n=10) to explore lived experiences, and Afrocentric sharing circles (n=2) to probe collective perspectives. Participants were purposefully recruited through community networks within African immigrant-serving community organizations. Results: Our findings demonstrate how various levels of the socio-ecological model (SEM) – individual (knowledge about gardening, busy schedules, and transportation challenges); interpersonal (not seeing people of their ethnicity on the garden); community (distance to the garden); environmental (extreme weather); and structural (inflation, unemployment/underemployment, import restrictions, systemic racism, and government bureaucracy) barriers to most immigrants. These factors interact to limit the maximum engagement of African immigrants in collective community gardening. However, participants who accessed collective gardens reported significant benefits, including maintaining healthy foodways, knowledge exchange, growing social capital, and community connections that support overall wellbeing. Conclusions: This study contributes an accessible framework for understanding and addressing the complex barriers that limit engagement in community gardens for vulnerable communities, while highlighting opportunities for creating more inclusive and culturally responsive urban agriculture initiatives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".