New immigrant settlement in a mid‐sized city: a case study of housing barriers and coping strategies in Kelowna, British Columbia
Bibliographic record
Abstract
The successful integration of immigrants into a new society is based on their attainment of several basic needs, including access to adequate, suitable and affordable housing. While this has long been a concern in Canadian cities, such as Vancouver, Toronto, and Montréal, it is also increasingly an issue in growing mid‐sized cities such as Kelowna, in the interior of British Columbia. While Kelowna's real estate market is one of the most expensive in the country, there is little published data or literature on the housing experiences of immigrants in the city. This study examines the housing experiences and stresses of a small group of immigrants in Kelowna's rental housing market. This study uses data from five focus groups with 34 new immigrants and 20 interviews with key informants, conducted in Kelowna in summer 2008. The evidence indicates that for this group of immigrant newcomers, the housing search process in Kelowna's rental housing market met with significant barriers in locating affordable rental housing. Of these barriers, the most commonly cited were: (a) high housing costs; (b) lack of reliable housing information, including lack of access to organizations that provide housing help (government or not); and (c) prejudice by landlords based on the immigrants' ethnic and racial background . This study points to the need for more comparative studies on the housing experiences of immigrants in mid‐sized cities in Canada to better understand which groups of immigrants are more successful than others in finding affordable housing in these mid‐sized cities, and why .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".