Do Static Risk Factors Predict Differently for Aboriginal Sex Offenders? A Multi-site Comparison Using the Original and Revised Static-99 and Static-2002 Scales
Bibliographic record
Abstract
There is much concern about the extent to which risk assessment tools designed to predict recidivism are equally valid for both Aboriginal and non-Aboriginal offenders. The current study compared Aboriginal and non-Aboriginal male sex offenders on items and total scores of the original and revised Static-99 and Static-2002 scales. The study included five independent Canadian samples with Static-99 and Static-99R scores (319 Aboriginals and 1,269 non-Aboriginals), three of which also had Static-2002 and Static-2002R scores (209 Aboriginals and 955 non-Aboriginals). Aboriginal sex offenders scored significantly higher than non-Aboriginal sex offenders on total scores and items indicative of general criminality and tended to score lower on items indicative of sexual deviancy. Static-99/R total scores and items generally predicted sexual recidivism with similar accuracy for Aboriginal and non-Aboriginal sex offenders. In contrast, significant differences were found for Static-2002/R total scores and several of their items, with lower predictive accuracy for Aboriginals. The results suggest that at least some items of the Static scales are not as predictive for Aboriginal as for non-Aboriginal sex offenders, with differences found on Static-2002/R rather than Static-99/R 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".