The sexual recidivism drop in Canada: A meta‐analysis of sex offender recidivism rates over an 80‐year period
Bibliographic record
Abstract
Abstract Research summary In the past, the Canadian government followed in the footsteps of its American counterpart by enacting “sex offender laws.” Since the 1990s, however, the Canadian criminal justice system has taken a different approach to the issue of sex offender recidivism (SOR), focusing on treatment, rehabilitation, and community risk management. This evidence‐based approach has been criticized for not doing enough to prevent convicted offenders from sexually reoffending. This criticism has not been addressed empirically, leaving open the question of whether this Canadian policy shift is associated with changes in the rate of sexual recidivism. The present study uses a meta‐analytic framework to look at 185 Canadian‐based studies involving over 50,000 offenders, making it possible to combine 226 sexual recidivism rates. After controlling for factors such as follow‐up length and the independence of samples, weighted pooled recidivism rates have declined since the 1970s by more than 60%. This trend may have gone unnoticed because it is not related to the year of publication but to the period in which the data were collected. Policy implications The findings have significant implications for criminal justice practices including the importance of using risk assessment tools that are regularly calibrated to reflect the evolution of sexual recidivism rates over time. Although the current study cannot provide firm conclusions about the factors responsible for this gradual drop, several hypotheses are discussed. Knowledge‐based criminal justice practices, better training for professionals, and improvements in treatment programs may have had a subtle and cumulative impact on sexual recidivism rates. The importance of examining period effects on SOR using a comparative and international perspective is 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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".