Newcomers in the Canadian Housing Market
Bibliographic record
Abstract
On a per capita basis, Canada maintains one of the largest immigration systems in the world. We know that a large majority of newcomers settle in a small number of places, mainly Montréal, Toronto, and Vancouver. These are places with low vacancy rates and, especially in Toronto and Vancouver, high real estate prices and rental fees. How are immigrants coping in the housing markets of Canada? Are they able to find suitable housing? At what cost, relative to their financial resources? What impacts are immigrants having on the housing markets of Canada? Previous studies of immigrants have found a clear pattern that applies to most groups: a progressive housing career. That is, the process of integration in Canada is associated with improving income levels, better housing, and rising rates of homeownership over time (Murdie et al 2006). After approximately 10 years in Canada, immigrants begin to overtake the Canadian-born in terms of home ownership and those who have been in the country longer actually have a higher level of ownership than the Canadian-born. In this way immigrants have a substantial impact on urban housing markets in Canada and may actually influence house prices, at least in Toronto and Vancouver (Carter, 2005). The improvement of housing conditions is a positive step in the integration experience, providing both psychological benefits and a sense of a stake in the country (Murdie and Tiexiera, 2003; Engeland and Lewis,
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.002 |
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".