Contested and (un)realized capabilities of wellbeing in rural places in Canada: Contemplating immigrants’ realities
Bibliographic record
Abstract
Rural relocation of immigrants in Canada has recently been touted as a way to support community wellbeing by mitigating the socioeconomic impacts of population decline common in many rural areas, while also fostering the wellbeing of immigrants through the provision of services, resources and support systems for ‘successful’ integration. Yet, discussions of rural resettlement often assume resource provision will effectively enable the successful flourishing of immigrants, eliding more complicated questions of social hierarchies, systemic obstacles, and the qualities of places that may enable or constrain flourishing wellbeing generative lives. Drawing on geographies of health, migration and development literatures, this paper examines the links between lived realities, capabilities, and rural places as immigrants contemplate resettlement outside of urban areas in Ontario. Based on data from focus groups with immigrants (n = 50) we reveal how rural places can (in)directly affect the conversion of individual skills and resources, some with potentially wellbeing generative realities, while others with degenerative dynamics of illbeing. We contend that immigrants’ contemplations and resettlement decisions eschew simple notions of choice, but are more complex and contradictory, shaped by competing wellbeing desires, uncertain place-based realities and temporal dynamics that operate at a variety of scales. Revealing immigrants’ contested capabilities underscores how place-based realities can create landscapes of uneven opportunities, rights and freedoms to attain wellbeing. The article concludes that future geographic research on health-migration-human development intersections should engage with contested capabilities by illuminating how places and their policies mediate settlement choices and the inequalities that surround them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".