Online Sexual Offending Against Children: Recidivism Rates and Predictors
Bibliographic record
Abstract
Recidivism among individuals who have sexually offended poses a significant public health and safety concern. It is crucial to assess the predictive validity of traditional risk factors in individuals engaged in online child exploitation. This study examines recidivism rates and risk factors among individuals involved in online child sexual exploitation, analyzing data from a sample of 228 adult males who had committed sexual and nonsexual offenses at their index crime. The findings suggest that offense-supportive cognitions (Harrell's C = .73-.75) and emotional congruence with children (Harrell's C = .77) serve as predictors for contact sexual recidivism. Consumption of child sexual exploitation material and bestiality pornography are linked to online sexual recidivism (.69 and .75, respectively) and negatively related to sexual recidivism (.29 and .32, respectively). Overall, this research contributes to a more nuanced understanding of recidivism patterns and risk factors among individuals engaged in online sexual offenses against children, emphasizing the need for tailored intervention strategies in this population.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".