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Record W2738431478 · doi:10.1177/0306624x17719289

Psychometric Properties in Forensic Application of the Screening Version of the Psychopathy Checklist

2017· review· en· W2738431478 on OpenAlexaff
Tamsin Higgs, Ruth J. Tully, Kevin D. Browne

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2017
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychopathyPsychopathy ChecklistChecklistPsychologyClinical psychologyComparabilityNormativeForensic scienceReliability (semiconductor)Antisocial personality disorderApplied psychologyPersonalityPoison controlInjury preventionMedicineSocial psychologyMedical emergency

Abstract

fetched live from OpenAlex

The Psychopathy Checklist: Screening Version (PCL: SV) is a short form of the Psychopathy Checklist-Revised (PCL-R), an expert-rated assessment that measures psychopathic personality traits in research, clinical, and community settings. The PCL-R is an extensively relied upon tool in psycho-legal contexts. The screening version is also widely used; however, it has received far less empirical attention than the PCL-R. This review examines the psychometric properties of the PCL: SV, specifically in relation to forensic samples, and evaluates its comparability with the full PCL-R. Previously reported similarity in the reliability and validity of the PCL: SV as established for the PCL-R was supported through further testing in forensic samples. However, limitations in terms of available normative data are highlighted, and the review engages with wider debate concerning the measurement of psychopathy.

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)0.010
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.456
GPT teacher head0.434
Teacher spread0.022 · 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
GenreReview

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

Citations13
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of Offender Therapy and Comparative CriminologySame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207