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Record W1964475685 · doi:10.1177/0886260515575606

Non-Homicidal and Homicidal Sexual Offenders

2015· article· en· W1964475685 on OpenAlexaffabout
Heng Choon Chan, Éric Beauregard

Bibliographic record

VenueJournal of Interpersonal Violence · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsExhibitionismPsychologyPedophiliaClinical psychologyParanoiaOffender profilingLogistic regressionBig Five personality traitsPsychopathologyPersonalityPoison controlInjury preventionPsychiatrySocial psychologyMedical emergencyMedicine

Abstract

fetched live from OpenAlex

This study aims to examine the psychopathological profile of non-homicidal sexual offenders (NHSOs) and homicidal sexual offenders (HSOs). Using an incarcerated sample of 96 NHSOs and 74 HSOs in a federal penitentiary in Canada, these offenders are compared in terms of their offending process, maladaptive personality traits, and paraphilic behaviors. A number of cross-tabular and sequential logistic regression analyses are performed. Relative to their counterpart, findings indicate that a higher percentage of HSOs select a victim of choice, report deviant sexual fantasies, mutilate their victim, and admit to their offense upon apprehension, whereas a higher percentage of NHSOs select victims with distinctive characteristics. In addition, a higher percentage of HSOs manifest paranoid, schizotypal, borderline, histrionic, narcissistic, obsessive-compulsive, and impulsive personality traits, and overall odd and eccentric personality traits compared with NHSOs. Similarly, a higher percentage of HSOs engage in exhibitionism, fetishism, frotteurism, homosexual pedophilia, sexual masochism, and partialism compared with NHSO. These findings are discussed with their implications for offender profiling.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.317
Teacher spread0.279 · 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
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

Citations52
Published2015
Admission routes2
Has abstractyes

Explore more

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