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Record W2999229420 · doi:10.1080/0047729x.2020.1712080

Military welfare in the Midland counties during and after the British civil wars, 1642-c.1700

2020· article· en· W2999229420 on OpenAlexaboutno aff
Stewart Beale

Bibliographic record

VenueMidland History · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpanish Civil WarIrishWelfareQuarter (Canadian coin)Poor reliefLawPopulationCivil disorderHistoryPolitical scienceCriminologySociologyPoison controlSuicide preventionDemographyMedicineDomestic violenceArchaeologyEnvironmental health

Abstract

fetched live from OpenAlex

The British and Irish civil wars of the mid-seventeenth century are estimated to have claimed the lives of the greatest proportion of the population in British history. This article assesses the social impact of the conflict by examining the welfare administered to war victims in the Midlands between 1642 and c.1700. Analysing the records of parliamentary county committees and county Quarter Sessions, it examines the ways in which war victims negotiated with the authorities for charitable relief, and how much money they received. The article highlights the strains placed on provincial officials tasked with maintaining the welfare system, and the hardships and bereavement the conflict inflicted on combatants and their families. The fact that some war victims were receiving relief as late as the 1690s demonstrates that the impact of the civil wars lingered for decades after the fighting had ceased.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.156
Teacher spread0.139 · 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 designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

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