A Billion Distorted Thoughts: An Exploratory Study of Criminogenic Cognitions Among Men Who Sexually Exploit Children Over the Internet
Bibliographic record
Abstract
There is evidence that endorsing a higher level of offense-supportive cognitions is associated with contact sexual offending. Such an association assumes the construct of cognitions as unidimensional, thus ignoring the possibility that specific subtypes of cognitions exist and that certain may be criminogenic. To investigate this possibility, this study aimed at examining the associations between criminal behaviors and cognitive themes found in the discourse of men who engage in sexual offenses against children over the Internet. Through the discourse of a sample of 60 men with online child sexual exploitation material and solicitation offenses, a previous study identified eight cognitive themes: Uncontrollability, Nature of harm, Child as sexual being, Child as partner, Dangerous world, Entitlement, Virtual is not real , and Internet is uncontrollable . These themes were not investigated for their criminogenic nature. Thus, in this study, bivariate analyses were used to determine whether these cognitive themes were linked to three indicators of criminal behaviors: the extent of criminal charges, the diversity of offending behaviors, and the nature of contact with victims. Results suggest that, taken as a whole, online sexual offense–supportive cognitions may not be criminogenic. Moreover, only cognitive themes related to antisocial orientation and atypical sexuality were found linked with criminal behaviors, although associations found remain limited. Findings and associated implications are further discussed for research and clinical purposes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".