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Record W2481046258 · doi:10.1057/9781137363909_7

Back to the Land: The Land Army after 1918

2014· book-chapter· en· W2481046258 on OpenAlexaff
Bonnie White

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsPeacetimeDemobilizationAgricultureGovernment (linguistics)Spanish Civil WarWork (physics)State (computer science)Agricultural landPolitical scienceEconomyEconomic growthDevelopment economicsEconomicsGeographyLawEngineeringPolitics

Abstract

fetched live from OpenAlex

Following the armistice on 11 November 1918, Britain’s food situation remained uncertain. The shift from a wartime to a peacetime economy would be carried out piecemeal and the return of men from the theatres of war would take many months to complete. Even once the demobilisation process was under way, there was no guarantee that the men who left agricultural work for military service would return to their pre-war employment. The agricultural industry was in a state of uncertainty. The depletion of the labour force in the decades prior to the war and Britain’s growing dependence on overseas markets was temporarily reversed between 1914 and 1918 as the domestic food economy was revitalised, but it was quite possible that these changes were temporary and the reliance on homegrown food would diminish again once prewar markets were restored. In November 1918, neither Talbot nor the Board of Agriculture knew for certain what lay ahead for the Women’s Land Army. Organisers hoped for the Land Army’s lengthy continuation, but knew that the organisation faced potentially insurmountable obstacles. The difficulty was in making the wartime organisation — which had marketed itself as a temporary, albeit necessary, instrument in the nation’s successful prosecution of the war — vital after 1918. The government was not unsympathetic to the Land Girls’ efforts — although the WLA represented a small percentage of the agricultural labour force, the 27,000 women of the Land Army voluntarily left their homes to settle in an unfamiliar area of the country where they endured months or years of hard labour. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.003
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: none
Teacher disagreement score0.039
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.005
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0380.008

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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