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Record W4414384814 · doi:10.22215/cujs.v5i2.5370

Care to Share: Emotional Language Use as a Function of Psychopathic Traits in Violent Youth Offenders

2025· article· en· W4414384814 on OpenAlexaff
Sarah Blakey, Adelle E. Forth

Bibliographic record

VenueCarleton undergraduate journal of science. · 2025
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychopathyEmpathyDark triadInterpersonal communicationPersonalityTraitBig Five personality traitsFunction (biology)

Abstract

fetched live from OpenAlex

Psychopathy is a personality disorder characterized by interpersonal, affective, behavioural, and antisocial characteristics. Notably, a lack of empathy and perceived deficits in emotion. As language is considered to be a habitual and reliable way in which emotion is expressed (Tausczik & Pennebaker, 2010), assessing emotional language use as a function of psychopathic traits can highlight differences in how emotion-related words are used by this population. In a sample of male incarcerated violent youth offenders assessed for psychopathy using the Psychopathy Checklist: Youth Version (Forth et al., 2003), the frequency, intensity, and polarity of emotion-related words and disfluencies in speech were explored using linguistic analysis software SEANCE (Crossley et al., 2017). Using both dimensional and categorical conceptualizations of psychopathy, more psychopathic traits were significantly associated with an increased frequency of emotion-related language and a lower use of filled pauses in speech. Results are contrary to emotion-deficit hypotheses of psychopathy, warranting further exploration into the verbal expression of emotions as representing interpersonal processes versus affective deficits.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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