Ethno-racial and nativity differences in access to affordable housing in Canada
Bibliographic record
Abstract
Canadians are experiencing a housing affordability crisis, but little attention has been paid to its ethno-racial and nativity disparities. Using data from the 2016 Canadian Census, we assess whether the likelihood of living in unaffordable housing (i.e., spending 30% or more of pre-tax income on housing costs) varies by ethno-race and nativity status, and identify the social factors contributing to these differences. We show that Middle Eastern and North Africans (MENAs) are most, and Whites are least, likely to live in unaffordable housing. Results from decomposition analyses suggest that MENA individuals’ high unaffordable housing rates are largely attributable to their high unemployment rates. The high unaffordable housing rates of East and South Asians are mainly associated with their higher propensity to live in urban areas with expensive housing. Immigrants are generally more likely than Canadian-born co-ethnics to live in unaffordable housing. Blacks and Southeast Asians are exceptions. As many governments take steps to address housing affordability crises, they should curtail the influence of structural barriers that preclude ethno-racial minorities from living in affordable 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".