Immigrants and homelessness—at risk in Canada's outer suburbs
Bibliographic record
Abstract
Homelessness is a risk for growing numbers of immigrants. Largely as a result of low incomes, newcomers are more likely than the Canadian‐born to spend over 50 percent of total household income on housing costs. Many newcomers suffer ‘hidden homelessness’. They do not use shelters and other services, but share accommodation, couch‐surf and rely on their social contacts for temporary and precarious housing. The adverse impact of low incomes on the housing experiences of Canadian newcomers is exacerbated in the outer suburbs of metropolitan areas where the supply of affordable housing is limited. This study explores the social backgrounds and housing experiences of immigrant households that are vulnerable to homelessness in outer suburbs through analysis of special tabulations from the 2001 census for York Region and interviews with representatives from local community organisations serving immigrant and low‐income populations. The initial findings confirm that a high proportion of newcomers in York Region are at‐risk of homelessness during the first 10 years of residence in Canada. Although renters are more vulnerable than homeowners, a substantial percentage of newcomers who are homeowners pay more than 30 percent of their total income on housing costs. The shortage of affordable rental housing in the outer suburbs exacerbates the impacts of low incomes, immigration status, household size and ethnoracial identities on immigrants' housing .
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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 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".