Psychopathy, emotionality, and offending
Bibliographic record
Abstract
The present research sought to examine the interrelations of emotion, crime characteristics, and self-reported psychopathy; and to examine criterion related validity of the Self-Report Psychopathy Scale Short-Form (SRP-SF). One hundred Canadian adult male offenders were interviewed with a series of structured questionnaires examining offense-related distress, shame, and guilt; offense instrumentality-reactivity; psychopathy; and institutional violence. Results revealed a significant negative association between SRP-measured psychopathy and offense-related guilt, but not offense-related shame or distress. Higher psychopathy scores were also associated with greater planning and control of the offense, higher levels of anger during the offense, and engagement in institutional violence. Receiver Operator Characteristic (ROC) analyses demonstrated SRP total, affective, and lifestyle facet scores yielded the strongest predictive accuracy for institutional violence followed by the interpersonal and antisocial facets. Results provide support for the predictive accuracy and construct validity of SRP-SF. Findings also reflect the instrumental-reactivity continuum of offenses with potential implications for the treatment and correctional needs of high psychopathy men, both in terms of emotional and interpersonal areas, in addition to the more traditionally targeted criminogenic foci. Researchers and clinician alike are encouraged to further explore this understudied topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".