Factors affecting housing-seeking difficulty for battered women: an investigation of racial discrimination and attitudes held by landlords
Bibliographic record
Abstract
Research has revealed that battered women often face difficulty when searching for long-term housing, which in turn, often leads to homelessness. Because housing discrimination has also been found in studies regarding racial prejudice, this research was designed to investigate a potential interaction between the effects of racial discrimination and discrimination against battered women in housing-seeking. In a telephone audit study, two confederates – one with a Canadian accent and one with a Caribbean accent – called 180 landlords who had advertised for a one-bedroom apartment, and disclosed one of three living conditions (i.e., a shelter for battered women, a friend’s house, no disclosure), while asking if the apartment was still available. The apartment was 4.50 times less likely to be reported as available when the confederate called claiming to be staying at a shelter than when she did not disclose a living situation. The accent of the confederate had no main effect and there was no interaction between accent and current living condition. A separate sample of 41 landlords was surveyed and asked for general views on battered women, risks that deterred them from wanting to rent to battered women, and sources for the information on which these concerns were based. The results of the studies suggest that landlords feel justified in discriminating against battered women; however, they are aware that racial discrimination is illegal and thus avoid this behaviour. Factors that could potentially make landlords more comfortable about renting to battered women are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".