A socio-spatial perspective on fostering a sense of belonging among refugee families resettled in Canadian small cities
Bibliographic record
Abstract
• Resettlement has a destabilizing effect on refugee families’ wellbeing • A sense of belonging nurtures psychosocial wellbeing • Place plays a critical role in fostering belonging in resettlement contexts • Refugees’ feelings of safety and security are intertwined with belonging and wellbeing • Belonging is developed through place attachment • Socio-spatial informed initiatives hold promise for fostering refugee belonging Fostering a sense of belonging plays a significant role in supporting refugee families’ integration into resettlement communities. The spatial dimensions within which refugees live and interact, such as homes, neighbourhoods, communities, and cities are influential in fostering belonging and supporting overall wellbeing. However, a socio-spatial perspective on belonging for refugees remains underdeveloped. To address this gap, we conducted an integrative knowledge synthesis of key sources from 2012 to 2022 contributing to the discourse on fostering refugee belonging via socio-spatial initiatives. Our aims were to highlight elements of belonging for refugee families that inherently embody the importance of place and to make recommendations for resettlement policy and practice. We focus on the size and socio-spatial characteristics of resettlement contexts to foster refugee families’ sense of belonging. Acknowledging the interconnectedness of belonging, notions of place, and integration can enhance resettlement processes and support the overall well-being of refugee families.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".