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Record W3041504989 · doi:10.1177/0190272520902105

Are Psychopathic Traits Associated with Core Social Networks? An Exploratory Study in University Students

2020· article· en· W3041504989 on OpenAlexaff
Kylie S. Reale, Martin Bouchard, Yan L. Lim, Alana N. Cook, Stephen D. Hart

Bibliographic record

VenueSocial Psychology Quarterly · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyDominance (genetics)PersonalityInterpersonal communicationInterpersonal relationshipSocial psychologyCore self-evaluationsDevelopmental psychologyBig Five personality traits

Abstract

fetched live from OpenAlex

In a sample of 480 university students, we examined associations between self-ratings of psychopathic traits, made using the Comprehensive Assessment of Psychopathic Personality (CAPP), the Psychopathic Personality Inventory: Short Form (PPI: SF), and self-ratings of the structure of their core social networks (i.e., best friends, intimates). Results indicated that higher self-ratings of domains (CAPP) and subscales (PPI: SF) related to interpersonal dominance, manipulation, poor attachment, and emotional regulation were associated with less connected core networks. We interpret the dominance and manipulation domain and subscale findings as preliminary evidence of a deliberate strategy to provide a more influential position within one’s social network. As for the associations with the attachment and emotional regulation domain and subscale findings, we suggest this could be reflective of deficits or a lack of desire both in establishing and maintaining long-term relationships.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.086
GPT teacher head0.359
Teacher spread0.274 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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