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Record W3194864397 · doi:10.1177/10731911211040108

Increasing the Utility of the Comprehensive Assessment of Psychopathic Personality–Lexical Rating Scale (CAPP-LRS): Instrument Adaptation and Simplification

2021· article· en· W3194864397 on OpenAlexaff
Katherine B. Hanniball, Richard E. Hohn, Erin K. Fuller, Kevin S. Douglas

Bibliographic record

VenueAssessment · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychopathyPsychologySet (abstract data type)PersonalityRating scaleAdaptation (eye)Scale (ratio)Field (mathematics)Applied psychologyDevelopmental psychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

The Comprehensive Assessment of Psychopathic Personality–Lexical Rating Scale (CAPP-LRS) is a self-report instrument designed to index psychopathy according to the CAPP psychopathy framework. Developed with the expressed goal of advancing the state of knowledge regarding the specific features of psychopathy, the CAPP model and associated instruments have garnered increasing attention and support in the field. Despite the conceptual strength of the CAPP model, the advanced lexical structure of its primary research tool (the CAPP-LRS) has led researchers to question the utility of the instrument for use with some populations of interest (e.g., forensic/correctional and adolescent/young adult samples). The aim of the present work was to address this issue by creating a lexically simplified, though functionally equivalent, version of the CAPP-LRS to increase accessibility to critically relevant populations. A set of two studies ( N = 602) describes the adaptation protocol and the initial validation of the modified instrument.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.072
GPT teacher head0.375
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

Citations5
Published2021
Admission routes1
Has abstractyes

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