Exploring Heterogeneity Among Deniers: Does Denial Predict Sexual Offender Recidivism Among Distinct Groups of Deniers?
Bibliographic record
Abstract
In the sexual offender literature, researchers have theorized numerous distinctions between groups of offenders who deny offence responsibility and varying reasons as to why they deny.However, few studies have empirically examined the heterogeneity of deniers or applicability of prior typologies.The purpose of the current study was to provide a more nuanced understanding of the heterogeneity of deniers through developing a profile of their risk using the Static-99R and VRAG-R.Results from a latent class analysis identified four distinct risk profiles, labeled Moderately Sexually Deviant (22.5%),Generally Antisocial (13.1%)Diverse Risk (27.6%) and Generally Low-Risk (36.7%).The risk profiles were then compared using pseudo-class draws methods, revealing differences in Attachment to convention and rates of sexual and sexual (including violent) recidivism.Similarities and distinctions between denier subgroups and prior theorized models of denial 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".