Estimating the probability of sexual recidivism among men charged or convicted of sexual offences: Evidence-based guidance for applied evaluators
Bibliographic record
Abstract
Risk assessment is routinely applied in forensic decision-making. Although relative risk information from risk scales is robust across diverse samples and settings, estimates of the absolute probability of sexual recidivism are not. Nonetheless, absolute recidivism estimates are still necessary in some evaluations. This paper summarizes research and offers guidance on evidence-based practices for assessing the probability of recidivism, organized largely around questions commonly asked in court. Overall, estimating the probability of sexual recidivism is difficult and should be undertaken with humility and circumspection. That being said, research favours empirical-actuarial risk tools for this task, more structured scales, and the use of multiple scales. Professional overrides of risk scale results should not be used under any circumstances. Paradoxically, however, professional judgement is still required in some circumstances. Risk scales do not consider all relevant risk factors, but the added value of external risk factors reaches a point of diminishing returns and may or may not be incremental (or worse, can degrade accuracy). There are reasons actuarial risk scales may both underestimate recidivism (e.g., undetected offending, short follow-ups) and overestimate recidivism (e.g., inclusion of sex offences not of interest in some referral questions, data on declining crime and recidivism rates, newer studies demonstrating overestimation of recidivism). Given all these considerations and the need for humility, in the absence of exceptional circumstances, I would not deviate too far from empirical estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".