Moving to Integration? The Origins of Chicago’s Gautreaux Program and the Limits of Voucher-Based Housing Mobility
Bibliographic record
Abstract
This article explores the origins and implementation of Chicago’s Gautreaux Assisted Housing Program, which used federal vouchers to move poor African American families to majority-white neighborhoods in the Chicago suburbs between 1976 and 1997. The program grew out of discrimination lawsuits by public housing tenants against the Chicago Housing Authority and Department of Housing and Urban Development. The plaintiffs originally sought the construction of new suburban public housing, but changes in federal policy, including the creation of vouchers, and shifts in civil rights jurisprudence confined them to the use of privately owned, existing housing. As a result, the remedy for state-sponsored segregation sent poor black families into a historically segregated housing market that remained largely unaltered by civil rights legislation. In addition, the program’s use of tenant screening and counseling services transformed the litigation from an attempt to reorganize metropolitan housing markets into a program of uplift and racial pioneering in white neighborhoods. This article underscores the ways in which market-based solutions, and the unspoken resistance to integration they sometimes fostered, are important to understanding not only the fate of the Gautreaux program but also the obstacles to civil rights litigation and suburban integration that emerged in the 1970s and beyond.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".