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Record W3210037376 · doi:10.5040/9781350130609

Shakespeare in the Theatre: Sir William Davenant and the Duke’s Company

2021· book· en· W3210037376 on OpenAlexfundno aff
Amanda Eubanks Winkler, Dominique Goy-Blanquet, Paul Menzer, Stephen Purcell, Robert Shaughnessy, Abigail Rokison-Woodall, Ayanna Thompson, Russell Jackson, Peter Kirwan, Stuart Hampton-Reeves, Conor Hanratty, Lucy Munro, Fiona Ritchie, Elizabeth Schafer, Richard Schoch

Bibliographic record

VenueBloomsbury Publishing Plc eBooks · 2021
Typebook
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsnot available
FundersYale UniversityUniversity of CambridgeArts and Humanities Research CouncilQueen's University BelfastQueen's UniversityFolger Shakespeare LibraryUniversity of Oxford
KeywordsArtHistory

Abstract

fetched live from OpenAlex

<JATS1:p>Eubanks Winkler and Schoch reveal how – and why – the first generation to stage Shakespeare after Shakespeare’s lifetime changed absolutely everything. Founder of the Duke’s Company, Sir William Davenant influenced how Shakespeare was performed in a profound and lasting way. This book provides the first performance-based account of Restoration Shakespeare, exploring the precursors to Davenant’s approach to Restoration Shakespeare, the cultural context of Restoration theatre, the theatre spaces in which the Duke’s Company performed, Davenant’s adaptations of Shakespeare’s plays, acting styles, and the lasting legacy of Davenant’s approach to staging Shakespeare.</JATS1:p> <JATS1:p>This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. It is funded by Queen’s University Belfast.</JATS1:p>

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.096
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0180.005

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 head0.027
GPT teacher head0.199
Teacher spread0.172 · 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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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