The Dynamics of Internet Sexual Solicitation: Examining the Criminal Careers of Online Groomers
Bibliographic record
Abstract
The criminal career approach has been widely employed in the context of sexual delinquency, contributing significantly to our understanding of the criminal activities of sex offenders. To date, no studies have examined the criminal trajectories of online groomers within the framework of a criminal career analysis. To address this gap, the primary objective of this paper is to analyze this group’s criminal trajectories to expand our knowledge on participation, frequency, duration, seriousness, and versatility. To achieve this, an LPA was thus conducted using a sample of 1201 online groomers. The results support the existence of multiple distinct trajectories followed by individuals who engage in online sexual solicitation of minors, revealing the presence of four distinctive profiles: one-timer groomers, versatile and late groomers, specialist sex offender groomers, and polymorphous and prolific groomers. The profiles differ based on the number and types of offenses committed, the duration of their criminal involvement, and the diversity of their criminal activities.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".