Exploring behavioral patterns of child-focused online sexual solicitation: findings from a Canadian sample
Bibliographic record
Abstract
BACKGROUND: This study investigates online sexual exploitation patterns of minors in Canada, using a dataset of 96 child luring cases from 2001 to 2020 provided by law enforcement. OBJECTIVE: The study aims to extend the understanding of online sexual exploitation by identifying and characterizing offender profiles based on their behavior. PARTICIPANTS AND SETTING: The study examines 96 cases of online sexual exploitation of minors in Canada, drawing from law enforcement data. METHODS: Latent Class Analysis (LCA) is applied to identify offender profiles. External validity analyses were conducted to assess the generalizability of the LCA model. RESULTS: Four profiles are identified: contact-driven (Profile 1), fantasy-driven with gradual progression (Profile 2), exhibitionist (Profile 3), and sex-driven (Profile 4). The study reveals that fantasy-driven offenders prioritize relationship-building before progressing to sexual activities. Contact-driven offenders, on the other hand, seek immediate sexual gratification. These findings indicate a significant heterogeneity in online offender behaviors. CONCLUSIONS AND LIMITATIONS: This study contributes to a deeper understanding of online sexual exploitation patterns targeting minors. It underscores the need for further research to adapt interventions and law enforcement strategies effectively in response to the complexities of online offender behaviors. One limitation of the study is the small sample size, which may affect the generalizability of the findings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".