Long Term Recidivism Rates Among Individuals at High Risk to Sexually Reoffend
Bibliographic record
Abstract
Preventive detention provisions in the US and Canada assume we can identify, in advance, individuals at high risk for sexual recidivism. To test this assumption, 377 adult males with a history of sexual offending were followed for 20 years using Canadian national criminal history records and Internet searches. Using previously collected information, a high risk/high need (HRHN) subgroup was identified based on an unusually high levels of criminogenic needs ( n = 190, average age of 38 years; 83% White, 13% Indigenous, 4% other). A well above average subgroup of 99 individuals was then identified based on high Static-99R (6+) and Static-2002R (7+) scores. In the HRHN group, 40% reoffended sexually. STATIC HRHN norms overestimated sexual recidivism at 5 years (Static-99R, E/O = 1.44; Static-2002R, E/O = 1.72) but were well calibrated for longer follow-up periods (20 years: Static-99R, E/0 = 1.00; Static-2002R, E/O = 1.16). The overall sexual recidivism rate for the well above average subgroup was 52.1% after 20 years, and 74.3% for any violent recidivism. The highest risk individuals (top 1%) had rates in the 60%–70% range. We conclude that some individuals present a high risk for sexual recidivism, and can be identified using currently available methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".