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Record W4212874371 · doi:10.1017/s0956793322000024

‘Scotland’s fighting fields’: the mobilisation of workers in rural Scotland during the Second World War

2022· article· en· W4212874371 on OpenAlexaboutno aff
Michelle Moffat

Bibliographic record

VenueRural History · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicScottish History and National Identity
Canadian institutionsnot available
Fundersnot available
KeywordsBattleWorkforceAgricultureGeographyAgricultural productivitySpanish Civil WarOddsPopulationRural areaEconomic growthSocioeconomicsPolitical scienceSociologyDemographyArchaeologyEconomicsLawMedicine

Abstract

fetched live from OpenAlex

Abstract As the Battle of the Atlantic threatened Britain’s importation of food and forestry supplies, authorities intensified plans to rapidly increase domestic production. In Scotland, this was a herculean task in rural communities decimated by land clearances, economic depression, and population decline. Against the odds, the mobilisation of a range of workers enabled Scottish agriculture and forestry to make impressive gains in production, and significantly impacted Scotland’s ability to meet wartime production targets. This article examines the contributions of four diverse groups of labourers that toiled in Scottish fields and forests: compelled labourers, including conscientious objectors and prisoners of war; adult and child volunteers; women; and foreign lumberjacks from Canada, Newfoundland, and British Honduras. This original research supplements our knowledge of the British rural workforce during the Second World War, and raises the issue of wartime migration and its effects on rural communities.

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)0.002
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 score0.183
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.007
Scholarly communication0.0040.001
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.016
GPT teacher head0.194
Teacher spread0.177 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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