Quantifying the relative risk of sex offenders: Risk ratios for Static-99R. Manuscript submitted for publication
Bibliographic record
Abstract
Given the widespread use of empirical actuarial risk tools in corrections and forensic mental health, it is important that evaluators and decision makers understand how scores relate to recidivism risk. In the current study, we found strong evidence for a relative risk interpretation of Static-99R scores using 8 samples from Canada, United Kingdom, and Western Europe (N = 4,037 sex offenders). Each increase in Static-99R score was associated with a stable and consistent increase in relative risk (as measured by an odds ratio or hazard ratio of approximately 1.4). Hazard ratios from Cox regression were used to calculate risk ratios that can be reported for Static-99R. We recommend that evaluators consider risk ratios as a useful, nonarbitrary metric for quantifying and communicating risk information. To avoid misinterpretation, however, risk ratios should be presented with recidivism base rates. Keywords risk ratio, relative risk, Static-99R, sex offenders Actuarial risk tools are structured methods for combining risk factors into a total score (or risk categorization) and provide empirically derived estimates of recidivism prob-abilities (Dawes, Faust, & Meehl, 1989). Risk tools are widely used in forensic psy-
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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 | 0.038 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".