Interpreting multiple risk scales for sex offenders: Evidence for averaging.
Bibliographic record
Abstract
This study tested 3 decision rules for combining actuarial risk instruments for sex offenders into an overall evaluation of risk. Based on a 9-year follow-up of 940 adult male sex offenders, we found that Rapid Risk Assessment for Sex Offender Recidivism (RRASOR), Static-99R, and Static-2002R predicted sexual, violent, and general recidivism and provided incremental information for the prediction of all 3 outcomes. Consistent with previous findings, the incremental effect of RRASOR was positive for sexual recidivism but negative for violent and general recidivism. Averaging risk ratios was a promising approach to combining these risk scales, showing good calibration between predicted (E) and observed (O) recidivism rates (E/O index = 0.93, 95% CI [0.79, 1.09]) and good discrimination (area under the curve = 0.73, 95% CI [0.69, 0.77]) for sexual recidivism. As expected, choosing the lowest (least risky) risk tool resulted in underestimated sexual recidivism rates (E/O = 0.67, 95% CI [0.57, 0.79]) and choosing the highest (riskiest) resulted in overestimated risk (E/O = 1.37, 95% CI [1.17, 1.60]). For the prediction of violent and general recidivism, the combination rules provided similar or lower discrimination compared with relying solely on the Static-99R or Static-2002R. The current results support an averaging approach and underscore the importance of understanding the constructs assessed by violence risk measures.
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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 | 0.263 | 0.473 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".