No place to call home: housing stigma against previous offenders and those who experience mental health concerns
Bibliographic record
Abstract
The current study examined the effect of criminal record (exoneree, releasee, no record) and mental health status (mental illness, no mental illness) on landlords' willingness to rent to individuals. I hypothesized that those with a mental illness would receive fewer responses from landlords than those without a mental illness, and both releasees and exonerees' would experience more discrimination than those without a criminal record. I also expected an interaction, whereby releasees and exonerees with a mental illness would face the greatest level of discrimination. A total of 1224 emails were sent utilizing six fake email addresses, which responded to online apartment listings across Canada posted on Kijiji. Of the 1224 email inquiries, we received 414 landlord responses (33.8%). The results showed that mental illness was the strongest predictor that a landlord would not respond (70.1%); response rates dropped to 33.1% if the tenant had a criminal record. Landlords were less likely to say "Yes" to an apartment being available (57.1%) when the prospective tenant disclosed a mental illness versus when they did not (85.0%). Taken together, these findings suggest that housing discrimination is prevalent, and that mental health status may be more impactful in landlords' decisions to rent to prospective tenants than a criminal record.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".