Examining Whether Practical Measures of Neighborhood Characteristics Improve Correctional Risk Assessment Tools
Bibliographic record
Abstract
This study examined whether inclusion of a neighborhood domain improved prediction and classification in an existing risk assessment tool. Logistic regression and analysis of covariance (ANCOVA) with random effects were conducted using a sample of individuals under community supervision ( N = 10,548) to determine whether a neighborhood domain improved the predictive validity of the Ohio Risk Assessment System-Community Supervision Tool (ORAS-CST). In five of our six models, inclusion of the neighborhood domain did not significantly improve the predictive validity of the ORAS-CST regardless of whether it was considered as an additive variable or moderator. One model found that the addition of a neighborhood domain improved prediction; however, the relationship was opposite from what was theoretically expected. The findings suggest that individual-level factors remain the most meaningful predictors in correctional risk assessment tools for those under community supervision. Future research is needed on whether neighborhood indicators improve assessment tools for individuals under other forms of supervision.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".