The Criminal Histories and Later Offending of Child Pornography Offenders
Bibliographic record
Abstract
The likelihood that child pornography offenders will later commit a contact sexual offense is unknown. In the present study, we identified a sample of 201 adult male child pornography offenders using police databases and examined their charges or convictions after the index child pornography offense(s). We also examined their criminal records to identify potential predictors of later offenses: 56% of the sample had a prior criminal record, 24% had prior contact sexual offenses, and 15% had prior child pornography offenses. One-third were concurrently charged with other crimes at the time they were charged for child pornography offenses. The average time at risk was 2.5 years; 17% of the sample offended again in some way during this time, and 4% committed a new contact sexual offense. Child pornography offenders with prior criminal records were significantly more likely to offend again in any way during the follow-up per-iod. Child pornography offenders who had committed a prior or concurrent contact sexual offense were the most likely to offend again, either generally or sexually.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".