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Record W4386714976 · doi:10.1108/jcp-04-2023-0019

Naked body disposal: an indicator of the type of sexual homicide

2023· article· en· W4386714976 on OpenAlexaff
Hana Georgoulis, Éric Beauregard, Julien Chopin

Bibliographic record

VenueJournal of Criminal Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité LavalInternational Centre for Comparative CriminologyUniversité de MontréalSimon Fraser University
Fundersnot available
KeywordsHomicidePsychologyDispose patternDismembermentSocial psychologyCrime sceneDating violenceCriminologyPoison controlHuman factors and ergonomicsEngineeringMedical emergencyDomestic violenceMedicinePolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to investigate whether sexual homicide offenders (SHO) who dispose of the victim’s body naked present with particular crime scene characteristics. Design/methodology/approach This study aims to answer this question through the use of a sequential logistic regression to test the individual effects of each set of crime scene variables against the manner of disposal using a sample of 662 solved cases of extrafamilial sexual homicide from an international database. Findings Results demonstrated that the modus operandi behaviors of sexual penetration, asphyxiation, dismemberment and overkill were significantly associated with the body being disposed of naked. In addition, removing or destroying evidence from the scene was also significantly associated with a naked victim. In contrast, the body was more likely to be dumped clothed if the contact scene was deserted and the victim was a stranger. These results suggest that SHOs who dispose of the body naked are more in line with the sadistic sexual murderer, while clothed victims are often disposed of by angry offenders. Originality/value To the best of the authors’ knowledge, this is the first study to examine the particular manner of disposing the victim’s body naked in cases of sexual homicide.

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)0.002
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 score0.824
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.086
GPT teacher head0.420
Teacher spread0.335 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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