Communicating risk for sex offenders: Risk ratios for Static-2002R. Sexual Offender Treatment
Bibliographic record
Abstract
Aim/Background. Actuarial risk tools are commonly used in corrections and forensic mental health settings. Given their widespread use, it is important that evaluators and decision-makers understand how scores on these tools relate to recidivism risk. Relative risk is one useful metric for communicating an offender's risk of reoffending. Methods. In the current study, risk ratios were computed for Static-2002R scores using 3 Canadian samples (N = 1,452 sex offenders). Results. Each increase in Static-2002R 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) and this increase was stable across time. Hazard ratios from Cox regression were used to calculate risk ratios that can be reported for Static-2002R. Conclusion. We recommend that evaluators and treatment providers consider risk ratios as a useful, non-arbitrary metric for quantifying and communicating risk information. Key words: risk ratios, relative risk, Static-2002R, sex offenders, risk communication Communicating an individual's risk of reoffending is an essential task for corrections and forensic mental health professionals. To evaluate the risk for crime and violence in these settings, actuarial risk tools are often utilized (Otto & Douglas, 2010). Consequently, it is important to be able to correctly interpret and effectively communicate the information provided by these actuarial risk tools.
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".