Similar Predictive Accuracy of the Static-99R Risk Tool for White, Black, and Hispanic Sex Offenders in California
Bibliographic record
Abstract
Although considerable research has found overall moderate predictive validity of Static-99R, a sex offender risk prediction tool, relatively little research has addressed its potential for cultural bias. This prospective study evaluated the predictive validity of Static-99R across the three major ethnic groups (White, n = 789; Black, n = 466; Hispanic, n = 719) in the state of California. Static-99R was able to discriminate recidivists from nonrecidivists among White, Black, and Hispanic sex offenders (all area under the curve [AUC] values >.70; odds ratios >1.39). Base rates (at a Static-99R score of 2) with a fixed 5-year follow-up across ethnic groups were very similar (2.4%-3.0%) but were significantly lower than the norms (5.6%). The current findings support the use of Static-99R in risk assessment procedures for sex offenders of White, Black, and Hispanic heritage, but it should be used with caution in estimating absolute sexual recidivism rates, particularly for Hispanic sex offenders.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".