Offense-Supportive Cognitions Expressed by Men Who Use Internet to Sexually Exploit Children: A Thematic Analysis
Bibliographic record
Abstract
Although offense-supportive cognitions are related to the maintenance of contact sexual offending behavior, it is unknown whether this finding also applies to online sexual offending behavior. A few studies have examined the cognitions of men convicted for using child sexual exploitation material, but findings remain limited due to important methodological limitations. Furthermore, fewer studies have investigated the cognitions of men who use the internet to solicit sexual activities with children. The objective of this study was to examine the nature of the cognitions that support online sexual offending against children. The content of police interviews was analyzed using a thematic analysis to identify the cognitive themes present in the offense-related views expressed by 20 men who consumed child sexual exploitation material, 15 who sexually solicited children, and 18 who committed both types of online offenses. Results revealed eight cognitive themes that reflected four underlying themes related to interpersonal relationships, the sexualisation of children, perceptions of the self, and perceptions of the virtual environment. Findings indicate that while the cognitive themes of the three groups are similar, their specific content varies according to the types of offenses. Implications for future research are discussed.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".