Characteristics and treatment of internet child pornography offenders
Bibliographic record
Abstract
In the realm of sexual offenses, there has been a decrease in hands-on offenses, but an increase in online offenses against children. The current issue is whether online and offline sexual offenders are alike or differ. This literature review investigates the differences among individuals who have committed child pornography offenses, individuals who have committed contact offenses against children, and individuals who have committed both. This review discusses the various typologies that have been proposed of those who have committed online offenses against children, the diagnostic implications of having committed child pornography offenses, and the current state of treatment and prevention of individuals who have committed online sex offenses against children. The studies examined were found from psychology databases, listserv links, and references of those collected articles. Only articles in English were included in the review. Overall, Internet child pornography offenders (ICPOs) tend to score significantly differently from contact offenders on various psychological measures. These findings may imply that ICPOs have different treatment needs than contact offenders.
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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 | 0.004 |
| 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".