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Record W2049794572 · doi:10.1177/0093854803261326

Some Misconceptions about the Hare PCL-R and Risk Assessment

2004· article· en· W2049794572 on OpenAlexaff
James F. Hemphill, Robert D. Hare

Bibliographic record

VenueCriminal Justice and Behavior · 2004
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsPsychopathyPsychopathy ChecklistChecklistConstruct (python library)PsychologyRecidivismConstruct validityMeasure (data warehouse)Risk assessmentApplied psychologyPoison controlPsychometricsClinical psychologySocial psychologyAntisocial personality disorderInjury preventionComputer sciencePersonalityMedicineComputer securityCognitive psychologyData miningEnvironmental health

Abstract

fetched live from OpenAlex

The Hare Psychopathy Checklist-Revised is a reliable and valid measure of a clinical construct, psychopathy. Its validation includes, but is not limited to, its role in risk assessment. Nevertheless, some commentators have questioned the validity of the Psychopathy Checklist-Revised because it does not consistently outperform purpose-built risk instruments. We contend that this view is misdirected and reflects a very narrow view of construct validation. The framework for our discussions is the conceptually and methodologically flawed “lesson in knowledge cumulation” recently proffered by Gendreau, Goggin, and Smith in which they arrived at the unwarranted conclusion that the Level of Service Inventory-Revised is generally superior to the Psychopathy Checklist-Revised for predicting recidivism and violence. We argue that both instruments are useful, but for different reasons. The Level of Service Inventory-Revised is a specialized risk tool, whereas the Psychopathy Checklist-Revised and its derivatives measure one of the most explanatory and generalizable risk factors identified to date.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.737

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 studies0.0010.000
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 head0.059
GPT teacher head0.378
Teacher spread0.319 · 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

Citations82
Published2004
Admission routes1
Has abstractyes

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