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Record W4400010244 · doi:10.1177/00938548241257604

One Size Doesn’t Fit All: An Exploratory Typological Approach to Understanding Criminal Career Heterogeneity in Intimate Partner Homicide

2024· article· en· W4400010244 on OpenAlexafffundabout
Olivier Péloquin, Julien Chopin, Francis Fortin, Jean‐Pierre Guay, Éric Chartrand, Sarah Paquette

Bibliographic record

VenueCriminal Justice and Behavior · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversité LavalSimon Fraser UniversityUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHomicideSituational ethicsDomestic violenceCriminal justicePsychologyCriminologyLaw enforcementIntimate partnerPoison controlSocial psychologyHuman factors and ergonomicsPolitical scienceMedicineMedical emergencyLaw

Abstract

fetched live from OpenAlex

Approximately one in seven homicides globally is committed by a partner within an intimate relationship. While criminology research on intimate partner homicide (IPH) perpetrators is extensive, their interactions with law enforcement remain underexplored. This study examines the criminal trajectories of IPH perpetrators to ascertain whether they exhibit common or diverse patterns. Utilizing data from Quebec's official criminal events database, the study analyzes variables concerning the criminal histories of 1,780 individuals involved in attempted or completed IPH through latent profile analysis. Findings indicate five distinct profiles among IPH perpetrators: one-time, low-volume intimate partner violence (IPV), moderate-volume IPV, high-volume violence, and high-volume polymorphous perpetrators. The external validity of these profiles is assessed using additional criminal career, contextual, and situational variables. Implications for the justice system's practices and challenges are also 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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.297
GPT teacher head0.407
Teacher spread0.110 · 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 designQualitative
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

Citations3
Published2024
Admission routes3
Has abstractyes

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