“You Are Not the Tenant I am Looking For”
Bibliographic record
Abstract
Research suggests that formerly incarcerated individuals, and individuals belonging to racial minority groups, experience stigma and housing discrimination. The current study explored landlords’ attitudes and differential communications toward formerly incarcerated individuals – particularly wrongfully convicted individuals – of varying races. Using data from an experimental audit study, we examined the content of landlords’ email responses to rental inquiries from fictitious convicted and wrongfully convicted individuals, and members of the general public (i.e., control), who were either Black, Indigenous, or White. A content analysis revealed three main themes: 1) responding with courtesy; 2) probing for additional information; and 3) willingness to set up a viewing. Logistic regressions revealed that landlords were more likely to justify the rental’s unavailability, inquire about the renter’s financial stability and references, and to say they would follow up later when corresponding with convicted and wrongfully convicted individuals compared to control. Landlords were also more likely to ask White renters about their criminal history compared to Black and Indigenous renters. Surprisingly, individuals belonging to racial minority groups were not disadvantaged further in this data. The findings are discussed in the context of post-incarceration support.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".