Immigrants and Refugees in the Housing Markets of Montreal, Toronto and Vancouver, 2011
Bibliographic record
Abstract
New data based on a linkage between the Immigrant Landing File and the 2011 National Household Survey are used to build a picture of immigrants and refugees in the housing markets of Canada’s three largest metropolitan areas. While most newcomers find it a challenge to secure affordable and adequate housing, Montreal, Toronto, and Vancouver have attracted different immigrant populations who are presented with distinct economic conditions and housing markets. As a result, there are some common patterns in housing consumption among immigrants across the three cities, but there are quite profound differences as well. The situation is particularly variegated when we examine the outcomes for specific immigrant admission categories and visible minority groups. In general, immigrants reach high levels of home ownership, especially in Toronto and Vancouver, and probably have a significant impact on the housing markets of the two cities. But there are also many who cannot find a comfortable foothold in the housing market. The experiences of refugees in the three cities are highlighted, and we find that, in the long term, refugees approach the total population in terms of home ownership levels and, also, the ratio of individuals under financial stress in the housing market. This rather positive story has only become apparent because of our access to new data, and suggests that we should reconsider the commonplace understanding of refugees as representing a long-term burden on Canadian society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".