Race, Gender, and Statistical Representation: Predatory Mortgage Lending and the US Community Reinvestment Movement
Bibliographic record
Abstract
American mortgage markets, once arenas of discrimination by exclusion, now operate as venues of segmentation and discrimination by inclusion: credit is widely available, but its terms vary enormously. One market segment involves sophisticated predatory practices in which certain groups of borrowers are targeted for high-cost credit that strips out home equity and worsens the risks of delinquency, default, and foreclosure. Unfortunately, it has become more difficult to measure inequalities of predatory lending: race–ethnicity and gender are ‘disappearing’ from the main public data source used to study, organize, and mobilize on issues of lending inequalities. In this paper, we present a mixed-methods case study of statistical representation of homeowners and homebuyers marginalized by race, ethnicity, and gender. A theoretical examination of official data-collection practices is followed by a discussion of alternative meanings of racial–ethnic and gender nondisclosure. Interviews with a sample of homeowners and homebuyers in the Washington, DC, area reveal some respondent ambivalence about the details of data-collection practices, but provide no consistent support for the idea that nonreporting is solely a matter of individual choice. Econometric analyses indicate that nondisclosure is driven primarily by lending-industry practices, with the strongest disparate impacts in African-American suburbs. Predatory lending is producing ambivalent spaces of racial-ethnic and gender invisibility, requiring new strategies in the reinvestment movement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".