<scp>S</scp>haky foundations: Refugees in Vancouver's housing market
Bibliographic record
Abstract
Abstract Our purpose in this article is to examine refugees' access to housing in metropolitan Vancouver. How are refugees faring in Vancouver's housing market relative to non‐humanitarian immigrants? Is there evidence that their housing circumstances change over time? Following from this, can we detect systematic differences in housing experiences between refugees selected and supported by the Canadian government versus those who come to Canada seeking asylum? Finally, what are the most important barriers for refugees in Vancouver's housing market and how are they addressed? Our study involved three main forms of data collection: focus groups with representatives of organizations that support newcomers, focus groups with newcomers, and a systematic survey exploring the housing experience of immigrants and refugees. While this article focuses on the survey results, we also draw upon the contextual knowledge obtained through the focus groups. We find that refugees are more likely than economic immigrants to lack the resources needed to access adequate and affordable housing. Many inhabit inadequately maintained, overcrowded, and unaffordable housing units and experience increased risk of homelessness. Therefore, we conclude that there is a need for greater coordination between housing and settlement policy to enable all newcomers to meet their housing needs.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".