Predicting recidivism among adult male child pornography offenders: Development of the Child Pornography Offender Risk Tool (CPORT).
Bibliographic record
Abstract
In this study, we developed a structured risk checklist, the Child Pornography Offender Risk Tool (CPORT), to predict any sexual recidivism among adult male offenders with a conviction for child pornography offenses. We identified predictors of sexual recidivism using a 5-year fixed follow-up analysis from a police case file sample of 266 adult male child pornography offenders in the community after their index offense. In our 5-year follow-up, 29% committed a new offense, and 11% committed a new sexual offense, with 3% committing a new contact sexual offense against a child and 9% committing a new child pornography offense. The CPORT items comprised younger offender age, any prior criminal history, any contact sexual offending, any failure on conditional release, indication of sexual interest in child pornography material or prepubescent or pubescent children, more boy than girl content in child pornography, and more boy than girl content in other child depictions. The CPORT was significantly associated with any sexual recidivism, with moderate predictive accuracy, and thus has promise in the risk assessment of adult male child pornography offenders with further cross-validation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".