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 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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.039 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".