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Record W2740056513 · doi:10.1111/jopy.12337

Comparing the lexical similarity of the triarchic model of psychopathy to contemporary models of psychopathy

2017· article· en· W2740056513 on OpenAlexafffund
Dylan T. Gatner, Kevin S. Douglas, Stephen D. Hart

Bibliographic record

VenueJournal of Personality · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychopathyPsychologySimilarity (geometry)Antisocial personality disorderCognitive psychologySocial psychologyPersonalityPoison controlArtificial intelligenceInjury preventionComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: The triarchic model of psychopathy (Patrick, Fowles, & Krueger, 2009) posits that psychopathic personality comprises three domains: boldness, meanness, and disinhibition. The present study aimed to clarify conceptual overlap between contemporary definitions of psychopathy, with particular emphasis given to the relevance of boldness (i.e., social dominance, venturesomeness, emotional resiliency)-a topic of recent debate. METHOD: Undergraduate students (N = 439) compared the lexical similarity of triarchic domains with two contemporary models of psychopathy: the Comprehensive Assessment of Psychopathic Personality (CAPP; Cooke, Hart, Logan, & Michie, 2012) and the Five-Factor Model of psychopathy (FFM; Widiger & Lynam, 1998). RESULTS: From a content validity perspective, meanness and disinhibition were lexically similar to both the CAPP and FFM psychopathy, whereas boldness was less strongly associated with these models. Meanness showed the strongest lexical similarity in comparison with past prototypicality ratings of the CAPP and FFM psychopathy. CONCLUSIONS: These findings bear implications for defining and comparing conceptualizations of psychopathy that underpin its assessment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.293
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.260
GPT teacher head0.393
Teacher spread0.133 · 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 teacher head, 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

Citations15
Published2017
Admission routes2
Has abstractyes

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