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Record W2085935121 · doi:10.1080/00223890701268074

Capturing the Four-Factor Structure of Psychopathy in College Students Via Self-Report

2007· article· en· W2085935121 on OpenAlexaff
Kevin M. Williams, Delroy L. Paulhus, Robert D. Hare

Bibliographic record

VenueJournal of Personality Assessment · 2007
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychopathyPsychologyDark triadConfirmatory factor analysisConstruct (python library)Nomological networkConstruct validityExploratory factor analysisPersonalityPsychometricsScale (ratio)Developmental psychologyClinical psychologySocial psychologyStructural equation modeling

Abstract

fetched live from OpenAlex

A number of self-report psychopathy scales have been used successfully in both clinical and nonclinical settings. However, their factor structure does not adequately capture the four factors (Interpersonal, Affective, Lifestyle, and Antisocial) recently identified in the Psychopathy Checklist-Revised (PCL-R; Hare, 2003) and related measures. This deficit was addressed by upgrading the Self Report Psychopathy Scale (SRP-II; Hare, Hemphill, & Harpur, 1989). In Study 1 (N = 249), an exploratory factor analysis of this experimental version revealed oblique factors similar to those outlined by Hare (2003). In Study 2 (N = 274), confirmatory factor analysis (CFA) confirmed this structure, that is, four distinct but intercorrelated factors. The factors exhibited appropriate construct validity in a nomological network of related personality measures. Links with self-reports of offensive activities (including entertainment preferences and behavior) also supported the construct validity of the oblique four-factor model.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.383
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations454
Published2007
Admission routes1
Has abstractyes

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