Migration to non-metropolitan communities: Community-based perspectives on immigrant settlement and multicultural diversity in a small city in Canada
Bibliographic record
Abstract
Immigrants in Canada are increasingly moving into non-metropolitan communities (smaller cities). Non-metropolitan communities are unique contexts of settlement due to a fabric of difference in the physical and social landscape in comparison to larger, multicultural cities that are renowned for immigration and diversity. Policies and immigration pathway programs in Canada are deliberate about promoting non-metropolitan communities as places for immigrant settlement due to the necessity of addressing demographic challenges, such as an aging or declining population. However, less is known about the experiences of settlement in a smaller city from the perspective of immigrants living in the community. This study explored the community-based perspectives about multicultural diversity and the reasons for migration to a non-metropolitan community in Canada. It involved interviews with immigrants from multicultural backgrounds, municipal officials, and a key informant on immigrant settlement and integration. The findings of this study indicated that economic and family-related factors were the primary reasons for migration to and settlement in a non-metropolitan community. This paper contributes to the literature on population and demographic change by emphasizing the importance of understanding place satisfaction from the perspective of diversity in the community as an approach to addressing demographic challenges in non-metropolitan communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".