The role of urban and rural greenspaces in shaping immigrant wellbeing and settlement in place
Bibliographic record
Abstract
Greenspaces promote mental and physical health, yet racialized immigrants are known to experience inequitable greenspace access. There is growing interest in the ability of greenspace to support immigrant settlement and wellbeing. This paper responds to the need for greater attention to equity and inclusion within greenspace and wellbeing studies by examining the unique experiences facing immigrant populations. Given increasing immigrant settlement into rural and suburban places globally we also address the lack of knowledge on urban and rural greenspace differences, despite known distinctions in place-based attributes (e.g., density, accessibility, level of diversity/xenophobia). We explore urban and rural greenspace experiences in one of the top immigrant-receiving countries in the world through focus group and interview data from immigrants living in a dense, highly diverse Canadian urban neighbourhood, in addition to an outlying rural community. These insights are complemented by perspectives from planners, decision-makers and designers with influence over greenspace development/management. Our findings contribute towards the development of more equitable and inclusive greenspace by addressing the dearth of knowledge on related experiences and impacts facing immigrant populations specifically. We illuminate challenges and/or assets involved in supporting immigrant wellbeing and settlement in greenspaces including factors unique to and/or exacerbated by urban or rural contexts. Place-based attributes (e.g., distance, connectivity, space, density, demographics, level of familiarity with diversity, presence of culturally inclusive infrastructure, environmental quality, etc.) must be better understood and managed through planning, design and decision-making to support wellbeing given their influence upon physical activity, social interactions, feelings of safety and belonging.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".