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Record W2973649958 · doi:10.1177/1079063219877173

To Reoffend or Not to Reoffend? An Investigation of Recidivism Among Individuals With Sexual Offense Histories and Psychopathy

2019· article· en· W2973649958 on OpenAlexaff
Pauline Leung, Jan Looman, Jeffrey Abracen

Bibliographic record

VenueSexual Abuse · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMinistry of Community Safety and Correctional ServicesKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsRecidivismPsychopathyPsychologyPsychopathy ChecklistPopulationSex offenderLogistic regressionClinical psychologyJuvenile delinquencyDevelopmental psychologyAntisocial personality disorderPoison controlDemographyPersonalityInjury preventionSocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Although psychopathy is a well-established risk factor for recidivism among those who have committed sexual offenses, there are nonetheless some individuals with sexual offense histories who are high in psychopathy but do not recidivate. This population-nonrecidivating psychopathic sex offenders (NRP-SOs)-was the focus of the current investigation. Data from 111 individuals with sexual offense histories who received a Hare Psychopathy Checklist-Revised (PCL-R) rating of at least 25 (suggesting the presence of psychopathy) were analyzed. With recidivism operationalized as the accrual of any new serious-that is, violent or sexual-charges, 39 recidivated (RP-SOs), whereas 72 did not (NRP-SOs). A logistic regression was conducted to assess whether NRP-SOs could be differentiated from RP-SOs. Being older at the time of release, a lesser criminal history, and being married predicted nonrecidivism. PCL-R factor scores and sexual deviance were not predictive. These findings highlight the heterogeneity that exists, even among those high in 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 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 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 score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.301
Teacher spread0.268 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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