Predictive accuracy of Static-99R across different racial/ethnic groups: A meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: The overrepresentation of numerous racial/ethnic groups in the criminal legal system warrants examination of the cross-cultural applicability of risk assessment tools. Static-99R is a tool used in diverse countries to assess sexual recidivism risk. We conducted a meta-analysis on the predictive accuracy of Static-99R across different racial/ethnic groups. HYPOTHESES: No hypotheses were made regarding discrimination, given that past research could support hypotheses of differential or equivalent accuracy. We hypothesized that Indigenous individuals would score higher on Static-99R than non-Indigenous or White individuals. METHOD: Our search identified 18 eligible documents (from 17 distinct studies) with 41 nonoverlapping effect sizes. These 17 studies examined the predictive accuracy of Static-99R with racially/ethnically diverse men charged with or convicted of sexually motivated offenses. We report analyses using both fixed-effect and random-effects meta-analysis. RESULTS: Indigenous and Black individuals scored significantly higher on Static-99R than their non-Indigenous or White counterparts, with small effect sizes. For discrimination, area under the curve (AUC) values were generally moderate-to-large and statistically significant for all groups in both fixed-effect and random-effects analyses. Within-study subgroup analyses indicated significantly lower accuracy for Indigenous and Hispanic individuals compared with White/non-Indigenous samples (though for Hispanic individuals, this finding was significant only in the fixed-effect analyses). No statistically significant differences in accuracy were found between White and Black individuals. Static-99R significantly predicted recidivism with large effect sizes across two samples of Asian individuals. Two studies supported calibration across Black, White, and Hispanic individuals. Two studies examining calibration of Static-99R for Indigenous individuals had mixed findings. CONCLUSIONS: Given a small number of studies and limitations with both the fixed- and random-effects analyses, readers should interpret findings regarding Hispanic individuals with caution. The analyses clearly found significant but lower accuracy for Static-99R with Indigenous individuals. Potential reasons for this differential accuracy are discussed, along with limitations of the meta-analysis and suggestions for research and practice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".