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Record W2747959507 · doi:10.1111/1556-4029.13584

Adverse Childhood Experiences and Criminal Extremity: New Evidence for Sexual Homicide

2017· article· en· W2747959507 on OpenAlexaffabout
Matt DeLisi, Éric Beauregard

Bibliographic record

VenueJournal of Forensic Sciences · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHomicidePoison controlOddsPsychologySexual abuseInjury preventionPsychiatrySuicide preventionChild abuseMedicineClinical psychologyMedical emergencyLogistic regression

Abstract

fetched live from OpenAlex

Adverse childhood experiences are associated with a wide range of behavioral, health, and psychiatric deficits and have recently been used to study the development of serious offending careers. Unfortunately, this research paradigm has largely ignored forensic populations. This study utilized the adverse childhood experiences framework to examine the associations between exposure to violence, victimization, and total adverse childhood experiences on sexual homicide using a sample of 616 incarcerated adult male sexual offenders from Canada 85 of whom committed sexual homicide. Epidemiological tables of odds revealed that a gradient of adverse childhood experiences was associated with sexual homicide, but that the most significant risks were for offenders who had the most extensive abuse histories. In adjusted models, exposure to violence, victimization, and total adverse childhood experiences increased the odds of sexual homicide by 334%, 249%, and 546%, respectively. These effects intensified in models adjusted for childhood enuresis, cruelty to animals, parental abandonment, deviant sexual behaviors, poor self-image, and sexual problems to 559%, 326%, and 849%, respectively. The adverse childhood experiences framework is a systematic way to organize the criminogenic developmental sequela in 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)0.001
metaresearch head score (Gemma)0.001
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 score0.768
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.140
GPT teacher head0.396
Teacher spread0.256 · 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

Citations54
Published2017
Admission routes2
Has abstractyes

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