A Peek into Their Mind? An Exploration of Links Between Offense-Supportive Statements and Behaviors among Men Who Sexually Exploit Children and Adolescents Online
Bibliographic record
Abstract
While forensic psychologists have some access to their patients’ thoughts when deciding on a diagnosis or appraising risk, others, such as police investigators, must rely on physical evidence and behavioral markers to make sense of a crime. Studies showing that offense-supportive cognitions constitute a risk factor for sexual offending, including offenses that take place on the internet, highlight the need for some access to offenders’ thoughts. This exploratory study examines the associations between offense-supportive statements about the sexual exploitation of children and adolescents and proxy behaviors. As part of PRESEL, a collaborative research project between Québec provincial police and academic researchers, the case files of 137 men convicted of using child sexual exploitation material or committing child-luring offenses were analyzed. Results showed that many meaningful risk factors and sexual offending behavioral markers were associated with the cognitive themes Sexualization of children, Child as partner, Dangerous world, Entitlement, and Uncontrollability. The use of encryption was negatively associated with the cognition Virtual is not real while Internet is uncontrollable was associated with fewer contacts with minors over the internet. Findings are useful for understanding the psychological needs that should be targeted in treatment, as well as helping prioritize police workloads.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 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".