Paraphilic Interests Versus Behaviors: Factors that Distinguish Individuals Who Act on Paraphilic Interests From Individuals Who Refrain
Bibliographic record
Abstract
= 3.18), were recruited through Amazon's Mechanical Turk. Participants completed questionnaires about their paraphilic interests and behaviors, as well as potential key factors linked to behavioral engagement (i.e., perceptions of consent, sexual excitation/inhibition, impulsivity, moral disengagement, empathy). Results indicated that higher moral disengagement and impulsivity, lower sexual control (i.e., high sexual excitation, low sexual inhibition), and maladaptive understandings of consent were best able to differentiate individuals who reported highly stigmatized (e.g., hebephilia, pedophilia, coprophilia) or Bondage and Dicipline, Dominance and Submission, Sadism and Masochism(BDSM)/Fetish paraphilic interests and engagement in the paraphilic behaviours associated with these interests relative to individuals who did not report such paraphilic interests or behaviors. Moreover, higher moral disengagement, impulsivity, and maladaptive perceptions of consent were best able to differentiate non-consensual paraphilic interests and behaviours (e.g., voyeurism, exhibitionism) compared to individuals who did not report these paraphilic interests or behaviours. These results provide future directions for the exploration of mechanisms that may contribute to engagement in paraphilic behaviors and may be targets for intervention aimed at preventing engagement in potentially harmful paraphilias.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".