Examining the effect of prison time on landlords' willingness to rent to exonerees: A test of the stigma‐by‐association framework
Bibliographic record
Abstract
Researchers posit that stigma-by-association may account for the discrimination that exonerees experience post-release. Exonerees who serve a longer prison sentence may experience more stigma than exonerees who spent less time in prison. Across two studies, we examined whether criminal history (exoneree, releasee, or control) or prison time (5 or 25 years) impacted landlords' willingness to rent their apartment. Authors responded to one-bedroom apartment listings in the Greater Toronto Area, Canada, inquiring about unit availability. The rental inquiries were identical except for criminal history and prison time. Across both studies, results demonstrated that landlords were significantly less likely to respond, and indicate availability, to exonerees and releasees compared to control. Landlords discriminated against exonerees when the exoneree did not mention a formal exoneration (Study 1) and explicitly mentioned that he was exonerated by DNA evidence (Study 2). Prison time had no significant impact. A content analysis of landlords' replies revealed that exonerees and releasees experienced more subtle forms of discrimination compared to individuals without a criminal history. Together, our results demonstrate that individuals who were formerly incarcerated and associated with prison-whether it be for 5 years or 25 years or a rightful or wrongful conviction-experience housing discrimination upon their release.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".