Race-Based Real Estate Practices and Spuriousness in Community Criminology: Was the Chicago School Part of a Self-Fulfilling Prophecy?
Bibliographic record
Abstract
For over a century, a small network of scholars, extending from the University of Chicago, shaped community criminology research. Drawing on human ecology, they argued that poor structural factors—poverty, ethnic heterogeneity, population mobility—cause crime. As this network studied crime in neighborhoods, another network changed neighborhoods. This other network also assumed structural factors were important, but its members developed policies and practices to alter them. This other network, also influenced by human ecology, was composed of real estate researchers. Historical records show that the two networks are connected. These connections raise the possibility of a self-fulfilling prophecy. The implications of the prophecy are that the correlations between structural factors and crime may be spurious. We show that real estate practices shaped structural factors. But the structural factors may not drive crime. Instead, real estate practices may have shaped crime opportunities through place management thus driving crime. This has serious implications for community criminology research. This theoretical paper lays out the historical evidence for this conjecture.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".