The Neuropsychology of Sex Offenders
Bibliographic record
Abstract
Typically, neuropsychological studies of sex offenders have grouped together different types of individuals and different types of measures. This is why results have tended to be nonspecific and divergent across studies. Against this background, the authors undertook a review of the literature regarding the neuropsychology of sex offenders, taking into account subgroups based on criminological theories. They also conducted a meta-analysis of the data to demonstrate the cognitive heterogeneity of sex offenders statistically. Their main objective was to test the hypothesis to the effect that the neuropsychological deficits of sex offenders are not broad and generalized compared with specific subgroups of participants based on specific measures. In all, 23 neuropsychological studies reporting data on 1,756 participants were taken into consideration. As expected, a highly significant, broad, and heterogeneous overall effect size was found. Taking subgroups of participants and specific cognitive measures into account significantly improved homogeneity. Sex offenders against children tended to obtain lower scores than did sex offenders against adults on higher order executive functions, whereas sex offenders against adults tended to obtain results similar to those of non-sex offenders, with lower scores in verbal fluency and inhibition. However, it is concluded that neuropsychological data on sex offenders are still too scarce to confirm these trends or to test more precise hypotheses. For greater clinical relevance, future neuropsychological studies should consider specific subgroups of participants and measures to verify the presence of different cognitive profiles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".