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Sexual homicide and violent offenders

2017· book· en· W2752680940 on OpenAlexaff
Éric Beauregard

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHomicideSituational ethicsCommitCriminologyPsychologyOffender profilingSexual assaultSexual violenceSocial psychologyPoison controlHuman factors and ergonomicsMedical emergencyEngineeringMedicine

Abstract

fetched live from OpenAlex

This essay explores whether the sexual murderer is a unique type of sex offender. Two competing hypotheses of sexual homicide are outlined: the unique type hypothesis and the situational hypothesis. The essay discusses the heterogeneity of sexual murderers and reviews the different typologies of sexual homicide and the most common types. Differences and similarities between sexual murderers and non-homicidal sex offenders are reviewed, focusing on offender characteristics, criminal career, situational factors, and modus operandi/crime characteristics. The essay examines the factors leading to a lethal outcome in sexual assaults by looking at the most important factors, and the combinations of factors, leading to the death of the victim. Misconceptions about the modus operandi of sexual murderers are discussed along with the main behaviors they use to commit their crime. Finally the essay explores the differences between serial and non-serial sexual murderers.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.038
GPT teacher head0.264
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes1
Has abstractyes

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