Child pornography offenders detected by surveillance of the Internet and by other methods
Bibliographic record
Abstract
BACKGROUND: Availability of child pornography on the Internet has created new opportunities for offending. It has been noted that many people charged with offences relating to this had not previously been identified as sexual offenders against children. AIM: Our aim was to compare the characteristics of people charged with child pornography offences as a result of police monitoring of the Internet with those detected by other means. We hypothesised that those apprehended via the Internet would be more likely to be older and less likely to have severe psychiatric disorder or to have been previously charged with a sexual offence involving contact with a child than those identified by other means. METHODS: Data were extracted from the findings of clinical examinations by the authors either in the course of preparing reports for court, or in the course of providing treatment. RESULTS: There were 52 men detected by police Internet surveillance and 53 men detected by other means, the latter including 16 men who had not been charged with an offence at the time of referral. Those detected via the Internet were more likely to be in possession of very large quantities of child pornography. Those detected by other means were more likely to have major psychiatric and substance abuse disorders and to report childhood sexual abuse. A subgroup analysis of the 89 people who were facing charges at the time of the assessment found that the only significant differences were in the amount of material and the history of sexual abuse. CONCLUSIONS: The men recruited to this study, conducted over a period of nearly 10 years, reflect the changing nature of the technology used to commit this type of offence in that time. The characteristics of the subjects did not confirm the stereotype of an Internet child pornography offender who was high functioning and otherwise well adjusted and carried a low risk of other types of offences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".