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Record W2462333452 · doi:10.1017/s0268416015000211

Famine and the female mortality advantage: sex, gender and mortality in northwest England, c. 1590–1630

2015· article· en· W2462333452 on OpenAlexaff
Jonathan Healey

Bibliographic record

VenueContinuity and Change · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsFamineStarvationDemographyGeographyHistoryDevelopment economicsSocioeconomicsSociologyBiologyEconomicsArchaeology

Abstract

fetched live from OpenAlex

ABSTRACT Studies of modern famines have found disproportionately high mortality amongst adult men. The most commonly suggested root of this ‘female mortality advantage’ is biological, and it seems to be strongest when starvation is the main cause of death. The present study is the first to investigate the phenomenon in an early-modern society. Looking at the famines of 1597 and 1623 in northwest England, it finds some evidence for a female mortality advantage in 1623, but that this was concentrated in the first 12 months of the crisis (after the 1622 harvest). The female advantage was also much greater in north Lancashire and Westmorland than it was in the wealthier western Lancashire plain. Together this supports the idea that there was actual starvation during the 1623 crisis, at least in these areas at these times. There are, however, some reasons to suppose that the most mortal phase of the crisis, around the winter of 1623–1624, took place at a time when food was becoming more widely available, and hence should be attributed to diseases that followed the famine.

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)0.001
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 score0.276
Threshold uncertainty score0.549

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.114
GPT teacher head0.268
Teacher spread0.154 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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