Even Highly Correlated Measures Can Add Incrementally to Predicting Recidivism Among Sex Offenders
Bibliographic record
Abstract
Criterion-referenced measures, such as those used in the assessment of crime and violence, prioritize predictive accuracy (discrimination) at the expense of construct validity. In this article, we compared the discrimination and incremental validity of three commonly used criterion-referenced measures for sex offenders (Rapid Risk Assessment for Sex Offence Recidivism [RRASOR], Static-99R, and Static-2002R). In a meta-analysis of 20 samples (n = 7,491), Static-99R and Static-2002R provided similar discrimination but outperformed the RRASOR in the prediction of sexual, violent, and any recidivism. Remarkably, despite large correlations between them (rs ranging from .70 to .92), these risk scales consistently added incremental validity to one another. The direction of the incremental effects, however, was not consistently positive. When controlling for the other measures, high scores on the RRASOR were associated with lower risk for violent and any recidivism. We also examined different methods of combining risk scales and found that the averaging approach produced better discrimination than choosing the highest score and produced better calibration than either choosing the lowest or highest risk score. The findings reinforce the importance of understanding the psychological content of criterion-referenced measures, even when the sole purpose is to predict a particular outcome and provide some direction concerning the best methods for combining risk scales.
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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 | 0.056 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".