Rural futures? Mapping newcomers' hopes about potential resettlement in Canadian rural areas
Bibliographic record
Abstract
Abstract The Canadian government recently launched initiatives to promote immigrant settlement outside of traditional gateway cities, in small towns and rural areas. These initiatives attempt to mitigate socio‐economic impacts of population decline, and address barriers to successful integration in urban areas. Drawing on the geographies of hope, this paper examines how newcomers navigate hopes as they imagine rural resettlement in Ontario. Based on focus groups with immigrants (n = 50), the findings suggest that newcomers' imagined rural futures are a dynamic and mobile process, shaped by competing hopes for a stable life. Rural imaginaries can sometimes provide a generative space to realize hopes and develop new future aspirations, other times they can constrain hopes for intergenerational futures. We contend that newcomer hopes arise in moments of relocation uncertainty, shaped by competing visions, interests, and priorities at individual and collective scales. Newcomers' expectations of rural futures are always enlivened with a sense of optimism for what has not yet become, but are equally replete with angst and anxiety for the future. This article concludes that future geographic research on migration‐hope‐place interactions, particularly in the health subfield, should engage constructions, experiences, and enactments of hope that mediate relocations and the policies governing 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".