A meta-analysis of recidivism rates among individuals who commit child sexual exploitation material (CSEM) offending
Bibliographic record
Abstract
A critical challenge for managing individuals with Child Sexual Exploitation Material (CSEM) offenses is addressing their risk of sexual recidivism, especially contact sexual offending. We report on a meta-analysis of 30 non-overlapping samples (total N = 25,978), with 26 samples identifying CSEM index offenses and subsequent recidivism using official sources (e.g., charges) and four samples identifying CSEM offenses and subsequent recidivism using self-report. Individuals with CSEM offenses based on official sources showed a fixed-effect recidivism rate of 5.9 % any sexual (95 % CI = [5.6, 6.3], k [studies] = 21, N = 19,112), 1.5 % contact sexual (95 % CI = [1.4, 1.7], k = 20, N = 18,543), and 4.1 % CSEM (95 % CI = [3.8, 4.4], k = 21, N = 13,522), after an average of 5-year follow-up. Based on official sources, the odds of contact sexual offenses among Mixed individuals (CSEM plus contact sexual offending) are 16 times higher than CSEM-Exclusive individuals (exclusively CSEM offenses in their sexual offending history) at 8.8 % versus 0.6 % (OR = 15.99), respectively. There were several other significant moderators: National sources of official recidivism data produced higher rates than local sources ( Q ∆ = 58.1, p < .0001, df = 1); official recidivism had lower rates than self-reported recidivism ( Q ∆ = 232.2, p < .0001, df = 1); longer follow-ups were associated with higher rates, unstandardized B = 0.01, Z = 75.8, p < .001; and more recent studies showed higher rates, unstandardized B = 0.002, Z = 68.0, p < .001. This meta-analysis establishes new recidivism base rates for individuals with CSEM offenses, which can be used to inform risk-driven policies and practices.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.058 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 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".