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Modernism after the Ballets Russes

2025· book· en· W4412393838 on OpenAlexaboutno aff
Gabriela Minden

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsModernism (music)ArtArt history

Abstract

fetched live from OpenAlex

Abstract Modernism after the Ballets Russes recovers the striking yet understudied role that Serge Diaghilev’s Ballets Russes played in the development of modernist theatre in Britain. Diaghilev’s company holds a renowned position in modernism across various arts. Yet its contributions to dramatic literature and dramaturgy have remained surprisingly elusive. This book establishes the Ballets Russes as an integral part of British theatre history, revealing how the company’s avant-garde repertoire inspired the creation of new composition strategies and performance techniques that privileged the immediacy of expression offered by the moving, dancing body. It shows that Diaghilev ballets provided new ways of thinking about the relationship between the literary and embodied aspects of dramatic performance, fuelling collaborations between eminent dramatists and theatre practitioners—Harley Granville Barker, J. M. Barrie, Terence Gray, and W. H. Auden—and lesser-known choreographers: Cecil Sharp, Tamara Karsavina, Ninette de Valois, and Rupert Doone. Through the prism of the Ballets Russes, this group of artists crafted distinctive new theatrical forms, including a whimsical terpsichorean fantasia and a politically subversive poetic–dramatic satire, as well as new methods of staging Shakespearean comedy and Attic tragedy. Together, this book contends, these literary and dramaturgical innovations represent a previously neglected strand of modernism: one that saw the dramatic power of the moving body expand the expressive resources of the period’s theatrical arts.

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.020
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.001

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 head0.021
GPT teacher head0.203
Teacher spread0.182 · 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
GenreOther

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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