Concordance and Discordance between Paraphilic Interests and Behaviors: A Follow-Up Study
Bibliographic record
Abstract
Although paraphilic interests represent significant risk factors for recidivism among sexual offenders, little is known about the magnitude of concordance between paraphilic interests and behaviors in the general population. The goal of this follow-up study was to conduct secondary analyses based on a sample of 1040 adults (475 men; 565 women) recruited in the general population. Levels of associations and active concordance (having both interest and experience), passive concordance (having neither interest nor experience), active discordance (having experience without interest) and passive discordance (having interest without experience) between paraphilic interest and corresponding behavior were assessed. Concordance and discordance indexes were also computed, as well as regressions and moderation analyses. As expected, paraphilic interests predicted corresponding behaviors, although the mean active concordance rate was only approximately 50%. Concordance rates varied with gender and the criminal nature (legal vs. illegal) of paraphilia. Paraphilic interests in adults from the general population may not have the same predictive value as that observed in medico-legal contexts. The possible role of other moderators in the concordance between paraphilic interest and behavior in non-clinical populations should be assessed. These findings have implications for sexual abuse prevention programs aiming at individuals in the community.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".