Examining the criminal history and future offending of child pornography offenders: An extended prospective follow-up study.
Bibliographic record
Abstract
We examined police occurrence and criminal records data for a sample of 201 registered male child pornography offenders originally reported by Seto and Eke (Sex Abus J Res Treat 17:201-210, 2005), extending the average follow-up time for this sample to 5.9 years. In addition, we obtained the same data for another 340 offenders, increasing our full sample to 541 men, with a total average follow-up of 4.1 years. In the extended follow-up of the original sample, 34% of offenders had new charges for any type of reoffense, with 6% charged with a contact sexual offense against a child and an additional 3% charged with historical contact sex offenses (i.e., previously undetected offenses). For the full sample, there was a 32% any recidivism rate; 4% of offenders were charged with new contact sex offences, an additional 2% of offenders were charged with historical contact sex offenses and 7% of offenders were charged with a new child pornography offense. Predictors of new violent (including sexual contact) offending were prior offense history, including violent history, and younger offender age. Approximately a quarter of the sample was sanctioned for a failure on conditional release; in half of these failures, the offenders were in contact with children or used the internet, often to access pornography again.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".