Geeks and Newbies: Investigating the Criminal Expertise of Online Sex Offenders
Bibliographic record
Abstract
While online sex offenders use a wide range of strategies to try to avoid police detection, attempts to avoid detection of child sexual exploitation materials (CSEM) and online sexual solicitation of children have received very little attention. This study aims to understand online sex offenders’ behaviors by modeling the factors associated with their use of technological data protection and anonymity preservation strategies. The data is based on a sample of 199 men involved in crimes related to the use of child pornography or sexual solicitation of minors online. The analytical strategy based on the use of an artificial neural network (ANN), a machine-learning system, identified two trends. First, those who displayed problematic substance use and sexual thoughts and fantasies as well as behaviors reported to be preoccupying did not use specific strategies to avoid police detection. Second, two combinations of factors predict use of police anti-detection strategy, suggesting that the criminal expertise of online sex offenders is manifested in two different patterns: those building on existing knowledge, and those learning skills through previous judicial experience.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".