The New Housing Segregation: The Jim Crow Effects of Crime-Free Housing Ordinances
Bibliographic record
Abstract
America is profoundly segregated along racial lines. We attend separate schools, live in separate neighborhoods, attend different churches, and shop at different stores. This rigid racial segregation results in social, economic, and resource inequality, with White communities of opportunity on the one hand and many communities of color without access to quality schools, jobs, transportation, or health care on the other. Many people view this as an unfortunate fact of life, or as a relic of legal systems long since overturned and beyond the reach of current legal process. But this is not true. On the contrary, the law continues to play a profound role in creating and legitimizing pat-terns of racial segregation all across America. Crime-free housing ordinances are one of the most salient examples of the role law plays in producing and sustaining racial segregation today. They are, in this respect, a critical mechanism for effectuating the new housing segregation. Crime-free housing ordinances are local laws that either encourage or require private landlords to evict or exclude tenants who have had varying levels of contact with the criminal legal system. Though formally race neutral, these laws facilitate racial segregation in a number of significant ways. This is the first article to explain precisely how they do so. The Article contends that crime-free housing ordinances enable racial segregation by importing the racial biases, racial logics, and racial disparities of the criminal legal system in-to private housing markets. While scholars have examined the important role local laws played in effectuating racial inequality, they have not paid attention to crime-free housing ordinances. In addition to foregrounding how crime-free housing ordinances reinforce and perpetuate racially segregated communities, this Article proposes an intervention: a “segregative effects” claim, an underutilized cause of action under the Fair Housing Act of 1968, to challenge this segregative impact. While this intervention would not end the pervasive nature of housing segregation across the United States, it could eliminate at least one of the causes of this persistent problem: a body of law whose formal race neutrality has obscured its racially segregative effects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".