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Record W2060135710 · doi:10.1097/paf.0b013e3181c2172a

Homicide and Suicide in Yorkshire and the Humber

2010· article· en· W2060135710 on OpenAlexaff
Marilyn Gregory, Christopher M. Milroy

Bibliographic record

VenueAmerican Journal of Forensic Medicine & Pathology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHomicideCohortPoison controlMedicineForensic scienceSuicide preventionDemographyPsychiatryMedical emergencySociologyInternal medicine

Abstract

fetched live from OpenAlex

Homicide-suicide (HS) events in Yorkshire and the Humber have been documented previously by Milroy in a study of the period 1975 to 1992 (Milroy, Med Sci Law. 1993;33:167-171; Milroy 1994; Milroy, Forensic Sci Int. 1995;71:117-122; Milroy, Med Sci Law. 1995;35:213-217; and Milroy, J Clin Forensic Med. 1998;5:61-64). Reported here is an update of that study covering HS events in the same region from 1993 to 2007. Data from cohort 1 (1975-1992) and cohort 2 (1993-2007) are presented and compared, where data are available, with the findings of 2 previous studies in England and Wales (Barraclough and Harris, Psychol Med. 2002;32:577-584; and West 1965). Homicide followed by suicide is often defined in the literature as homicide(s) followed by the suicide of the perpetrator within 1 week of the homicide(s) (Barraclough and Harris, Psychol Med. 2002;32:577-584; Campanelli and Gilson, Am J Forensic Med Pathol. 2002;23:248-251; and Hannah et al, 1998;19:275-283). All the cases reported here fall within this definition. Findings are consonant with international literature, and suggest that HS is most likely to be carried out by an older, white, married, or cohabiting working man, who kills his female partner and/or their children and then himself. There are indications that restricting access to significant methods of killing can reduce the incidence of HS.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.009
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.289
Teacher spread0.282 · 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 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

Citations19
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Forensic Medicine & PathologySame topicHomicide, Infanticide, and Child AbuseFrench-language works237,207