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Record W2598783781 · doi:10.1177/0886260517698824

Is Investigative Awareness a Distinctive Feature of Sexual Sadism?

2017· article· en· W2598783781 on OpenAlexaffabout
Kylie S. Reale, Éric Beauregard, Melissa Martineau

Bibliographic record

VenueJournal of Interpersonal Violence · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSadistic personality disorderPsychologyHomicidePoison controlSuicide preventionHuman factors and ergonomicsInjury preventionCriminologyPersonalityMedical emergencyMedicineSocial psychologyPersonality disorders

Abstract

fetched live from OpenAlex

Sadistic offenders are often described as individuals who are forensically aware, who carefully plan their offenses, and preselect the location of their crime. Despite this emphasis on strategies to avoid police detection, no study has specifically examined whether this behavior or trait was specific to sadistic offenders. Utilizing a sample of 350 cases of sexual homicide from Canada, sadistic sexual homicide offenders (SHOs) are compared with nonsadistic SHOs on their investigative awareness. Results from logistic regression analyses show that sadistic SHOs are more likely to use various precautions to avoid detection and select a deserted location, in comparison with nonsadistic offenders. In addition, sadistic SHOs, despite having a lesser time to body recovery than nonsadistic offenders, are more likely to see their case remain unsolved. Implications for the assessment of sexual sadism as well as for the police investigations will be discussed.

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)0.005
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.370
Teacher spread0.314 · 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

Citations36
Published2017
Admission routes2
Has abstractyes

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