Internet sexual solicitation of children: a proposed typology of offenders based on their chats, e-mails, and social network posts
Bibliographic record
Abstract
Although researchers have examined sexual solicitation of minors online, there is limited research on the content and patterns of victim-offender chats. These chats have potential use in investigations for triaging and prioritising cases, enhancing understanding of offenders, developing treatments, and crafting education and policy to prevent sexual solicitation of minors online. As part of a broader effort on Internet crimes against children (ICAC), we examine offender chat logs, email threads, and social network posts from state and local task forces on ICAC for a sample of 200 offenders in communications with undercover officers. We use mixed-methods analyses to identify key elements in these cases and propose a typology of online solicitation offenders: cybersex-only offenders, schedulers, cybersex/schedulers, and buyers. These findings provide support and expansion of existing research on offender types using a larger and more geographically diverse sample. Implications for research, practice, and policy are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".