Temporal order of sexual offending is risk-relevant for individuals with child sexual exploitation materials offences
Bibliographic record
Abstract
The current study examined the extent to which the temporal order of sexual offending may be risk-relevant for men with Child Sexual Exploitation Material (CSEM; also referred to as child pornography) offences. We categorized 85 men who had committed two distinct sexual offences (CSEM or contact sexual offence) into three groups: (1) 47% (n = 40) followed a stable pattern, that is, men with CSEM offences who then committed a new CSEM offence; (2) 41% (n = 35) followed a de-escalation pattern, that is, men with contact sexual offences who then committed a CSEM offence; (3) and 12% (n = 10) followed an escalation pattern, that is, men with CSEM offences who then committed a contact sexual offence. Compared to the other groups, the stable group had more sexual interest in children, the de-escalation group had a younger age at first police involvement and more prior offending, and the escalation group had more substance use problems. We then examined recidivism (any new offence after the second sexual offence) and found that the escalation group had the highest 5-year and 7-year reoffending rates (start of follow-up: opportunity after the second sexual offence) for any crime, any non-sexual violence, any violence (including contact sexual offences), and any contact sexual recidivism. The de-escalation and stable groups had the highest CSEM recidivism rates. The current study suggests that ordering of offending within men adjudicated for CSEM offences is risk-relevant and that those who fit the escalation pattern may be at higher risk to reoffend.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".