An Assessment of Long-Term Risk of Recidivism By Adult Sex Offenders: One Size Doesn’t fIt All
Bibliographic record
Abstract
Numerous instruments are available to clinicians for evaluating sex offenders’ reoffense risk. Although they have demonstrated effectiveness in predicting recidivism significantly better than unstructured clinical evaluation, little is known about their predictive accuracy in subgroups of sexual offenders or in the long term. This study was undertaken to evaluate the predictive accuracy of nine instruments (VRAG, SORAG, RRASOR, Static-99, Static-2002, RM2000, MnSOST-R, SVR-20, PCL-R) among three groups of sexual offenders across a 15-year follow-up period. The results indicate that these instruments yielded marginal to modest predictive accuracy for sexual recidivism. A more detailed study of aggressor subgroups indicated that in both the short and the long term, these instruments were more effective at predicting the sexual recidivism of child molesters and the violent and nonviolent recidivism of rapists. Finally, although mixed offenders sexually reoffend more often and more rapidly than do rapists or child molesters, firm conclusions cannot be drawn because of the small number of mixed offenders in the sample.
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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 | 0.005 | 0.015 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".