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Record W2994653326 · doi:10.1080/13552600.2019.1697831

The consistency of sexual homicide characteristics and typologies across countries: a comparison of Canadian and Scottish sexual homicides

2019· article· en· W2994653326 on OpenAlexaffabout
Sara Skott, Éric Beauregard, Rajan Darjee, Melissa Martineau

Bibliographic record

VenueJournal of Sexual Aggression · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
FundersEconomic and Social Research Council
KeywordsHomicideBivariate analysisPoison controlPsychologyInjury preventionHuman factors and ergonomicsSample (material)Suicide preventionOccupational safety and healthMultivariate analysisDemographyClinical psychologyMedicineEnvironmental healthSociologyStatisticsMathematics

Abstract

fetched live from OpenAlex

ABSTRACTAlthough similar subtypes of sexual homicide have been described cross-nationally, no study has directly examined whether two samples from different jurisdictions are comparable. This study therefore aimed to examine whether any substantively meaningful subtypes of sexual homicide cases could be identified in each sample, and if so, whether these subtypes were similar across jurisdictions. Two samples of male sexual homicide offenders were compared: a Scottish sample (n = 89) and a Canadian sample (n = 150). Subtypes were identified in each sample using LCA, identifying a 3-class solution in each sample. Despite differences between samples on the bivariate level, two very similar subtypes (Controlled-Organized and Diverse) emerged in both samples. Despite differences at the bivariate level, the similarities at the multivariate level indicate similarities in underlying offence pathways which underpin heterogeneity in sexual homicide offenders. The similarities between the subtypes identified suggests potential universality of types of sexual homicides cross-nationally.

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 score0.099
Threshold uncertainty score0.928

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.336
Teacher spread0.304 · 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

Citations22
Published2019
Admission routes2
Has abstractyes

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