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Record W7008878586

Defining the Heart of Darkness: A Psychometric and Behavioral Analysis of the Relationship Between Psychopathy and Sadism

2020· other· en· W7008878586 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPsychopathyPsychopathy ChecklistAntisocial personality disorderAggressionDark triadPersonalityBig Five personality traitsPoison control
DOInot available

Abstract

fetched live from OpenAlex

Psychopathy and sadism, personality constructs largely characterized by antagonistic tendencies, share several similar traits and behaviors such as cruelty, callousness, and antisocial behavior (Foulkes, 2019; Hare, 2003). They are two of the most severe risk factors for criminal offending and aggression across a wide range of contexts, rendering these traits major public health concerns (Foulkes, 2019; Kiehl & Hoffman, 2011; Reidy et al., 2015). Despite the risks they pose, these constructs are relatively poorly understood. It is unclear how much of psychopathy can be accounted for by sadism and vice versa. It is possible that sadism and psychopathy reflect two distinct constructs, each with its own defining features, factor structures, and nomological networks, which would have important implications for theory, practice, and policy (e.g. risk assessment, parole hearings). Yet, the degree of overlap and distinction between these traits has yet to be empirically and thoroughly examined. The overarching goal of the present dissertation project is to investigate the degree of overlap between psychopathy and sadism across three levels of analysis: psychometric, behavioral, and physiological. This project has the potential to provide valuable insights to theories of antagonistic personality traits and improve clinical and forensic assessment procedures. Foulkes, L. (2019). Sadism: Review of an elusive construct. Personality and Individual Differences, 151, 109500. Hare, R. D. (2003). The Hare psychopathy checklist revised. Toronto, ON, Canada: Multi-Health Systems, Incorporated. Kiehl, K. A., & Hoffman, M. B. (2011). The criminal psychopath: History, neuroscience, treatment, and economics. Jurimetrics, 51, 355. Reidy, D. E., Kearns, M. C., DeGue, S., Lilienfeld, S. O., Massetti, G., & Kiehl, K. A. (2015). Why psychopathy matters: Implications for public health and violence prevention. Aggression and Violent Behavior, 24, 214-225.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.319
Teacher spread0.278 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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