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Record W4313216565 · doi:10.1016/j.ehb.2022.101216

Surviving the Deluge: British servicemen in World War I

2022· article· en· W4313216565 on OpenAlexafffund
Roy E. Bailey, Timothy J. Hatton, Kris Inwood

Bibliographic record

VenueEconomics & Human Biology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInfantryOfficerDemographyAction (physics)Socioeconomic statusFirst world warMilitary serviceHistoryPolitical scienceSociologyAncient historyLawPopulation

Abstract

fetched live from OpenAlex

We estimate the correlates of death and injury in action during the First World War for a sample of 2400 non-officer British servicemen who were born in the 1890s. Among these 13.1% were killed in action and another 23.5% were wounded. Not surprisingly we find that the probability of death or wounding increases with time in the army and was higher among infantrymen. For a serviceman who enlisted in the infantry at the beginning of the war and continued in service, the probability of being killed in action was 29% and the probability of being either killed or wounded in action was 64%. We examine, for ordinary soldiers, the hypothesis that death and injury was more likely for those from higher socioeconomic backgrounds as is suggested in the literature on the 'lost generation'. While such selectivity applies when comparing officers with other ranks it does not apply among the ordinary soldiers who comprised 95% of the army.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.855

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.291
Teacher spread0.269 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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