Anti-black rental housing discrimination in the multicultural city? A field experiment in Toronto, Canada
Bibliographic record
Abstract
This study investigates anti-black rental housing discrimination in Toronto, challenging the notion that Canadian cities are immune to such discrimination. Using a first-contact audit methodology with racially signaled profiles, we analyzed 715 rental inquiries across diverse neighborhoods. Results show significant racial disparities, with black tenants receiving fewer positive responses. Importantly, racial-material decisions by landlords are more pronounced in affluent neighborhoods, and wealthy non-white neighborhoods even exhibit greater insulation from black tenants. Black tenants were also more likely to receive favorable responses for high-rent properties, and when landlords faced urgent vacancies, highlighting how economic pressures can sometimes override racial biases. These results challenge the assumption that such discrimination is strictly an American issue and underscore the need for Canada to critically engage with its own racial bias dynamics. It calls for robust anti-discrimination efforts and targeted policies, including stricter enforcement of anti-discrimination laws, regular audits, and landlord training to mitigate unconscious biases and promote inclusive, equitable housing in Canada’s multicultural urban environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".