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Record W3022411231 · doi:10.1177/0886260520916844

Sexual Sadism: Its Role in the Crime-Commission Process of Sexual Homicide of Children

2020· article· en· W3022411231 on OpenAlexaff
Julien Chopin, Éric Beauregard

Bibliographic record

VenueJournal of Interpersonal Violence · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsHomicidePoison controlCriminologySuicide preventionInjury preventionCommissionHuman factors and ergonomicsPsychologyOccupational safety and healthSexual assaultMedical emergencyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

This study investigates the role of sexual sadism in the crime-commission process of sexual homicide (SH) involving child victims. A comparison between sadistic and nonsadistic cases involving child victims is conducted by examining the crime context, crime characteristics, methods of killing, body recovery characteristics, and forensic awareness strategies used by offenders. The sample comes from the Sexual Homicide International Database (SHIelD) including 135 cases of solved SHs involving child victims-35 cases with sexual sadism and 101 cases without sexual sadism. The Sexual Homicide Crime Scene Rating Scale for Sexual Sadism (SADSEX-SH) scale is used to identify sexual sadism from crime scene actions. Bivariate and multivariate analysis are performed to examine the differences between the two groups. Findings indicate that sadistic SH of children are characterized by an important level of structured premeditation, the commission of more diversified sexual acts, the use of specific method of killing, and the partial use of forensic awareness strategies. Practical implications in terms of criminal investigations are 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 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 score0.350
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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

Citations30
Published2020
Admission routes1
Has abstractyes

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