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Record W2903625267 · doi:10.1111/ehr.12821

The gender division of labour in early modern England

2018· article· en· W2903625267 on OpenAlexaboutno aff
Jane Whittle, Mark Hailwood

Bibliographic record

VenueThe Economic History Review · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersLeverhulme Trust
KeywordsDivision of labourWork (physics)Quarter (Canadian coin)CraftNew englandWomen's workEconomySociologyDemographic economicsGender studiesPolitical scienceHistoryEconomicsLawEngineeringArchaeology

Abstract

fetched live from OpenAlex

Abstract This article presents new evidence of gendered work patterns in the pre‐industrial economy, providing an overview of women's work in early modern England. Evidence of 4,300 work tasks undertaken by particular women and men was collected from three types of court documents (coroners’ reports, church court depositions, and quarter sessions examinations) from five counties in south‐western England (Cornwall, Devon, Hampshire, Somerset, and Wiltshire) between 1500 and 1700. The findings show that women participated in all the main areas of the economy. However, different patterns of gendered work were identified in different parts of the economy: craft work showed a sharp division of labour and agriculture a flexible division of labour, while differences of gender were less pronounced in everyday commerce. Quantitative evidence of early modern housework and care work in England indicates that such work used less time and was less family‐based than is often assumed. Comparisons with gendered work patterns in early modern Germany and Sweden are drawn and show strong similarities to England. In conclusion it is argued that the gender division of labour cannot be explained by a single factor, as different influences were at play in different parts of the economy.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0020.001
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 head0.069
GPT teacher head0.236
Teacher spread0.167 · 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 designQualitative
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

Citations53
Published2018
Admission routes1
Has abstractyes

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