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Record W2928203215 · doi:10.1093/ehr/cez110

Exhibiting War: The Great War, Museums, and Memory in Britain, Canada, and Australia, by Jennifer Wellington

2019· article· en· W2928203215 on OpenAlexaboutno aff
Mark Connelly

Bibliographic record

VenueThe English Historical Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionState (computer science)Variety (cybernetics)CombatantInterpretation (philosophy)Government (linguistics)World War IIPresentation (obstetrics)HistoryPolitical scienceMedia studiesSociologyLawArt history

Abstract

fetched live from OpenAlex

Jennifer Wellington’s study of the presentation of the First World War through exhibitions of artefacts and visual images in Australia, Britain and Canada provides a fascinating insight into the home fronts of all three nations. Through the examination of a wide range of primary sources from the national (and other) archives of her three chosen examples, and insightful interpretation, synthesising and interweaving of other studies, she deepens considerably our growing understanding of the complex relationships between the state and citizens during the course of the conflict. Over recent years, the history of the home front has become much more focused on the concept of self-mobilisation and the degree to which people worked towards the agenda of the state under relatively little direct, centralised authority. By the same token, it is acknowledged that, by the end of 1916, war’s realities had made themselves fully felt, requiring the combatant states to enter a process of remobilisation in the second half of the conflict. Wellington’s work reveals the variety of those approaches used to remobilise society, while also highlighting the continuing importance of agencies and organisations that were linked to central government by dotted lines, rather than under its direct control.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.251
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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