Sex Offender Recidivism: Some Lessons Learned From Over 70 Years of Research
Bibliographic record
Abstract
Sex offender recidivism (SOR) has been the subject of research for over 70 years. Myths, misconceptions, and erroneous conclusions about SOR, however, remain widespread, impeding the development of evidence-based policies aimed at preventing sexual offenses. To address the rich but uneven literature, a comprehensive review was conducted making it possible to provide a contextualized overview of scientific knowledge against the backdrop of methodological issues, challenges, and shortcomings. Over the years, researchers have been asked to provide a simple answer to a seemingly simple question: what are the recidivism rates for sexual offending? In response, the field has produced a wide range of findings making it difficult to draw firm conclusions, leaving room for interpretation and personal biases. The variations in recidivism rates are attributable to offender and methodological characteristics, both of which are embedded in a particular sociolegal context. As a result, the base rate of SOR is more effectively considered in terms of a series of questions that should include the type of recidivism, with whom, over what period, and in what context. Issues and debates that have marked the field and fueled its growth are highlighted. Research innovations and important areas of research are also discussed.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".