The Sexual Recidivism Rates of Women Are Still Low: An Updated Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Compared to men, women are less likely to sexual offend. Previous reviews found low rates of sexual recidivism among women. The last published meta-analysis was based on studies from before 2010. AIMS: Conduct an updated meta-analysis of the sexual recidivism rates of women returned to the community. We expected the rates to be low and to decline the longer they remained sexual offence free in the community. METHODS: Fourteen studies met selection criteria. Their publication/presentation dates ranged from 1998 to 2023. Results were presented as raw proportions as well as meta-analytic averages. RESULTS: Of the 4208 women, 3.1% (131) were known to have sexually reoffended. The rate was 2.4% during the first 5 years (64/2642, k = 8) and 1.1% between years 5 and 10 (6/535, k = 2). There was large and significant variability across studies (prediction intervals: < 0.001%-11%). The rates of violent recidivism (7.8%) and general (any) recidivism (30.1%) were substantially higher than the rate of sexual recidivism. CONCLUSIONS: This review confirms previous findings that the sexual recidivism rate of women is very low. Their risk is so low that it is unlikely to be reduced by sexual crime specific treatment or public protection measures (e.g., registration and notification). Instead, gender-responsive interventions should focus on the women's risk for general criminal recidivism and strive to promote successful reintegration.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.039 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".