Northern migration: A case study of Latin American immigration, settlement, and housing experiences in Kelowna, a mid-sized city in the interior of British Columbia (Canada)
Bibliographic record
Abstract
Immigration from Latin American countries to Canada is a relatively recent phenomenon. Approximately half a million Latin Americans currently live in Canada. They tend to prefer urban areas and settle in Toronto, Montreal, and Vancouver. In the past, smaller and mid-sized cities in the interior of British Columbia were generally off the radar for immigrants, but this has changed since the early 2000s, and the city of Kelowna has gradually emerged as a popular destination, including for immigrants from Latin America. It is a growing population that has received relatively little scholarly attention. This article addresses this research gap by exploring the settlement and housing experiences of Latin American immigrants in the mid-sized city of Kelowna. The study draws on data from questionnaire surveys that were administered to 62 Latin American immigrants in the city of Kelowna in the summer-fall of 2018. The findings revealed that transitioning from their homelands was a stressful and costly experience for participants, particularly with regard to finding affordable housing. They reported enormous financial stress, with most living in unaffordable housing; more than half were spending more than 30% of their monthly income on housing. Participants’ residential mobility and housing searches were constrained in part by low vacancy rates, language barriers, lack of public transportation, and a lack of affordable housing to rent or buy. Immigrants’ unfamiliarity with how subsidized housing works in Canada, including how to access it, combined with a limited supply of this housing, are major challenges.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".