Ethnic Diversity, Immigrant Settlement, and Municipal Planning in a Small Canadian City: The Case of Brooks, Alberta
Bibliographic record
Abstract
Immigration in Canada has been characterized by an uneven distribution, with a disproportionate concentration of immigrants settling in major gateway cities. Despite numerous policies and programs developed by various levels of government to attract and retain immigrants towards smaller cities, less is known about planning for immigrant settlement through the lived experiences of immigrants in smaller cities. This study explored the place-based experiences of immigrants living in a smaller city and the municipality’s role in attraction and retention. Specifically, the questions of inquiry involved: 1) How do racialized immigrants experience settlement and integration in a smaller Canadian city? 2) What are the implications of lived experience when planning for diversity? Case study research of Brooks, Alberta was conducted involving interviews with racialized immigrants, municipal officials, and local settlement agency staff. This study found that the primary reason for immigrant settlement in a smaller city was based on economic or family-related factors. Furthermore, fostering a sense of belonging is critical for retention, and this can be facilitated through inclusive community spaces and partnerships between municipalities and communities. Planners should seek to understand the lived experiences of immigrants in community especially in contexts undergoing processes of social demographic changes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.048 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".