Exploring neighborhood transformations and community gardens to meet the cultural food needs of immigrants and refugees: A scoping review
Bibliographic record
Abstract
International migration is contributing to changes in the sociocultural and the economic landscapes of many cities in the world. As part of the changes in cities, we are witnessing an increased use of community gardens as spaces for wellbeing restoration, for social connection, and for addressing the challenge of food insecurity, particularly cultural food insecurity. Cultural food security is one major under-recognized issue, yet is pivotal to address given its role in positively supporting immigrants’ settlement and health. However, there is lack of comprehensive evidence of how neighborhoods are changing to accommodate these initiatives and meet the cultural food needs of diverse communities. Our scoping review explored evidence from existing literature on how neighborhoods are changing to accommodate community gardens (CG) as a novel means to address cultural food insecurity among immigrant communities and support place-making and resettlement. Our literature search identified several areas of transformations including the different kinds of community gardens that have emerged, the associated physical, social, cultural, environmental, economic, and policy changes that have been reported in other countries in the Global North. The review also identified multifold benefits of CG in this regard, including benefits to health and wellbeing – the physical (i.e. nutrition and physical activity), mental (e.g., a place for healing for immigrants fleeing war-torn countries, domestic violence, trauma; fostering a connection to the land in new environments via place-making), and social (e.g., fostering community connections and cultural knowledge exchange). Despite the known benefits of community gardens to immigrants' health and wellbeing, there remains a lack of comprehensive evidence in Canada on how neighborhoods are changing to accommodate these initiatives and meet the cultural food needs of diverse communities. Such studies will serve as sources of evidence for novel ideas to address the cultural food needs and food insecurity of immigrant communities, which is becoming a growing public health concern. • Neighborhoods are transforming to accommodate community gardening and resettlement of newcomers. • The transformations are physical, social, economic, cultural, environmental, and in government policies. • Community gardens are creating new spaces that are health restorative for newcomers. • Community gardens foster settlement via ‘a taste of home’ of culturally-familiar produce. • Limited academic evidence exist on how policies are shifting with the changes in neighborhoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".