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Record W2149442541 · doi:10.1177/1088767901005001003

Siblicide and Seniority

2001· article· en· W2149442541 on OpenAlexaffabout
Martin Daly, Margo Wilson, Catherine Salmon, Mariko Hiraiwa‐Hasegawa, Toshikazu Hasegawa

Bibliographic record

VenueHomicide Studies · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsSimon Fraser UniversityMcMaster University
Fundersnot available
KeywordsSenioritySiblingHomicideCommitDemographyPoison controlInjury preventionPsychologyMedicineMedical emergencyDevelopmental psychologySociologyPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

This article uses samples of siblicide from Canada, Great Britain, Japan, and Chicago to explore the possible relevance of seniority in siblicide. The tendency for the killer to be the younger party was especially true of cases in which victim and killer were same-sex adults and, especially, brothers close in age. The older party was much more likely to be the killer when one or both were children, but this tendency is adequately accounted for by the changing age-specific likelihood that one will commit a homicide at all. Only the Japanese data set contains information on actual birth orders, which were not demonstrably related to the likelihood of either killing or being killed by a sibling. An analysis of the Canadian data suggests that the rate of siblicide is unaffected by the age difference between siblings. The substance of lethal sibling conflicts is discussed in the light of these results, case descriptions, and literature on nonlethal sibling conflict.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.128
GPT teacher head0.416
Teacher spread0.288 · 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 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

Citations47
Published2001
Admission routes2
Has abstractyes

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