Settlement and Rental Housing Experiences Among Recent Immigrants in the Suburbs of Vancouver: Burnaby, Richmond, and Surrey
Bibliographic record
Abstract
Abstract The suburbanization of immigrants in Canada is a relatively recent phenomenon, and these suburban settlement experiences and residential patterns are varied and complex. This chapter explores the settlement and housing experiences of recent immigrants in Burnaby, Richmond, and Surrey – three culturally diverse and fast-growing Vancouver suburbs – as well as the interactions between suburbanization processes and the housing strategies of migrants. Securing good-quality, affordable housing is key to the successful resettlement and integration of immigrants. For this study, data were collected from questionnaire surveys administered to 137 immigrants renting in the suburbs of Vancouver. The findings revealed that transitioning from their homelands was a stressful and costly experience. Given the escalating housing costs in the rental and homeownership markets and low vacancy rates, most participants had difficulties finding housing. Participants coped by sharing housing with relatives or friends to save money or by renting a basement. They also reported financial stress, with most living in unaffordable rental housing. The low vacancy rates in Vancouver’s suburbs have created a ‘landlord’s market’ and about one-third of participants reported perceived discrimination based on income, large family size, immigrant status, and general mistrust of their cultural, religious, racial, or ethnic backgrounds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".