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Record W2883505630

A 'great anti-war play' : Bury the Dead on the world stage

2018· article· en· W2883505630 on OpenAlexaboutno aff
Lisa Milner

Bibliographic record

VenueePublications@SCU (Southern Cross University) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsStage (stratigraphy)Project commissioningHistoryPublishingPolitical scienceLawBiology
DOInot available

Abstract

fetched live from OpenAlex

With its compelling story of dead soldiers refusing to be buried as a protest against war, Irwin Shaw's 1936 experimental play Bury the Dead met with instant success on the New York stage. While it was eagerly taken up by mainstream and little theatres in the USA, it has most often been staged by radical and left-wing theatres throughout the world, including in Australia, Great Britain, India, South Africa and Canada. It resonated with audiences, and it also made waves: at an early British performance, members of the audience had to be treated for shock. Shaw drew his inspiration from the horrors of World War I and the Spanish Civil War, and the play's anti-war message and experimental style proved to be popular with Depression-era audiences fearing another world war. Its relevance has not diminished since that era, its success as a tool for moral protest and social commentary continuing to the present day, in many translations and nations. Its production, too, has been in varying forms. This article investigates the continuing attractiveness of Bury the Dead as an anti-war drama across a variety of historical, cultural, political and production contexts from 1936 to 2018, and interrogates the play's relevance for theatres in disparate times and places. In focusing on the play's Australian productions, it also provides a comprehensive production history of the work in this country.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.010
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.036
GPT teacher head0.278
Teacher spread0.243 · 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
Published2018
Admission routes1
Has abstractyes

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Same venueePublications@SCU (Southern Cross University)Same topicMilitary History and StrategyFrench-language works237,207