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Record W1968745783 · doi:10.1177/0093854810361617

Psychopathy and Risk Taking Among Jailed Inmates

2010· article· en· W1968745783 on OpenAlexaff
Marc T. Swogger, Zach Walsh, Carl W. Lejuez, David S. Kosson

Bibliographic record

VenueCriminal Justice and Behavior · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Center for Research ResourcesNational Institute of Mental Health
KeywordsPsychopathyAntisocial personality disorderPsychologyClinical psychologyAssociation (psychology)Dark triadHuman factors and ergonomicsPoison controlInjury preventionPersonality disordersPersonalityPsychiatryDevelopmental psychologyMedicineSocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex

Several clinical descriptions of psychopathy suggest a link to risk taking; however the empirical basis for this association is not well established. Moreover, it is not clear whether any association between psychopathy and risk taking is specific to psychopathy or reflects shared variance with other externalizing disorders, such as antisocial personality disorder, alcohol use disorders, and drug use disorders. In the present study we aimed to clarify relationships between psychopathy and risky behavior among male county jail inmates using both self-reports of real-world risky behaviors and performance on the Balloon Analogue Risk Task (BART), a behavioral measure of risk taking. Findings suggest that associations between externalizing disorders and self-reported risk taking largely reflect shared mechanisms. However, psychopathy appears to account for unique variance in self-reported irresponsible and criminal risk taking beyond that associated with other externalizing disorders. By contrast, none of the disorders were associated with risk taking behavior on the BART, potentially indicating limited clinical utility for the BART in differentiating members of adult offender populations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.333
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.

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

Citations54
Published2010
Admission routes1
Has abstractyes

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