Self-report psychopathy-III facet scores predict sexual crimes, sexual preferences, and sexual deviance index validity more precisely than total scores
Bibliographic record
Abstract
Understanding the profiles of sexual offenders, such as the presence of psychopathic traits, is key to preventing future sexual crimes. The self-report psychopathy-III (SRP-III) is a tool used to assess the characteristics of psychopathy, but improvements on its interpretation are required to maximize its precision. The SRP-III can be interpreted by examining the scores on each of the four facets (interpersonal manipulation, callous affect, erratic lifestyle, antisocial behavior), on each of two factors (factor 1, factor 2), or by examining the total score. Here, we investigate the interpretation of the results from the SRP-III using these three approaches of analysis of the data for predicting types of sexual crimes, sexually deviant preferences (measured via PPG), and the validity of the sexual deviance results. Logistic regressions were carried out using either the four facets, two factors, or the total score of the SRP-III. Data were previously obtained from 198 Canadian men who were convicted of, or who admitted to committing, at least one sexual crime, or who reported experiencing sexually deviant fantasies. We also examined the point-biserial correlations between each of the methods of interpreting the SRP-III results and each of the dependent variables. We find that SRP-III facet scores most precisely predict types of sexual crimes, sexually deviant preferences, and sexual deviance index validity, followed by SRP-III factor scores, and lastly SRP-III total scores. Additionally, significant correlations are only found between SRP-III scores and one dependent variable. Potential reasons for this are discussed. Based on these findings, we recommend that future studies consider facet and factor scores in addition to the standard practice of examining total scores.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".