Searching for Housing as a Battered Woman: Does Discrimination Affect Reported Availability of a Rental Unit?
Bibliographic record
Abstract
Individual battered women have reported experiencing housing discrimination, but the extent of this problem has not been examined. This research used two experiments and a survey to determine if landlord discrimination could keep women from accessing rental units. In Study 1, a confederate asked 181 landlords about the availability of a rental unit in one of three living conditions (shelter, friends, no mention of current living conditions) and across two scenarios (does or does not have a child). Rental units were almost 10 times more likely to be available in the control condition compared to the shelter condition, χ 2 (1, N = 181) = 8.624, p = .003, and these results were not affected by whether or not the caller had a child, χ 2 (1, N = 181) = 0.214, p = .644. In Study 2, the confederate was employed and left a message on 92 landlords' answering machines in the same three living conditions. The hypothesized comparison between the shelter and the other two conditions combined was significant, χ 2 (1, N = 92) = 4.602, p = .032. Finally, in a telephone survey of 31 landlords, a substantial minority (23%) said they would not rent to a hypothetical battered woman. The results of our studies suggest that discrimination against battered women by landlords is a real problem that is likely contributing to the difficulties that women experience in finding safe and affordable long-term housing.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".