Planning with multicultural diversity in small cities in Canada: a case study of immigrant lived-experiences in Brooks, Alberta
Bibliographic record
Abstract
Immigration, ethnic settlement, and global migration are processes which shape diversity in Canadian cities. There is rich literature vis-à-vis immigrant settlement in large, gateway cities. Less is known about the lived-experiences of visible-minority immigrants in small cities and the implications of planning with diversity in those contexts. This research explored the challenges and opportunities of visible-minority immigrants settling in a small city from a place-based and integration perspective and explored the role of municipalities in attracting and retaining immigrants in small cities. Case study research of Brooks, Alberta was conducted involving interviews with visible-minority immigrants, a municipal official, and a local immigration organization staff. This study had reinforced the primary reason of immigrant settlement in small cities were based on economic and family-related factors, but highlight the role of the municipality in retaining immigrants in small cities is to cultivate an inclusive community by acclaiming diversity through a place-based approach. Key Words: Multicultural Planning, Municipal Policy Diversity, Immigrant Lived-Experiences, Small Cities, Attract and Retain Immigrants, Visible-Minority Immigrants
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".