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Record W3181463261 · doi:10.21428/cb6ab371.57a21e01

Making sense of senseless murders: The who, what, when, and where?

2021· preprint· en· W3181463261 on OpenAlexaff
Kylie S. Reale, Éric Beauregard, Julien Chopin, Nathan Wells

Bibliographic record

VenueCrimRxiv · 2021
Typepreprint
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsRoyal Canadian Mounted PoliceSimon Fraser University
Fundersnot available
KeywordsSense (electronics)CriminologyPsychologyComputer securityComputer scienceEngineering

Abstract

fetched live from OpenAlex

The phenomenon of senseless or motiveless homicide refers to homicides that lack an objective external motivation. Despite the unique challenges these homicides pose to police, few empirical studies have been conducted on the topic and existing studies are limited to clinical studies using small samples. To overcome existing empirical shortcomings, the current study used a sample of 319 homicide cases no motive was established during the investigation to describe the who (offender and victim characteristics), (modus operandi, crime characteristics), where (encounter, crime, and body recovery associated locations), and when (time of the crime) of the entire criminal event. Findings provide insight into the entire crime-commission process and suggest a different dynamic to senseless homicide from what has been described in previous literature. Implications for police investigative practice are 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.362
Teacher spread0.272 · 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.

Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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