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Record W4416099624 · doi:10.1093/res/hgaf083

DEBORAH C. PAYNE. <i>The Business of English Restoration Theatre, 1660–1700</i>

2025· article· en· W4416099624 on OpenAlexaff
Diana Solomon

Bibliographic record

VenueThe Review of English Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Architecture and Urbanism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBusiness English

Abstract

fetched live from OpenAlex

‘If we build it, they will come’ seems to have been the perspective taken by managers Thomas Killigrew and William Davenant to reviving London theatre after the 1660 restoration of the monarchy. And yet, despite pent-up demand, technical innovations, talented playwrights, and the rise of the actress, London theatre between 1660 and 1700 was, financially speaking, hardly a runaway success. Few playwrights could make a living writing for the theatre, plays rarely experienced long runs, and during a 12-year stretch demand was not high enough to sustain more than one theatre company. In grappling with this state of affairs, Deborah Payne advances a bold and surprising argument: that Restoration theatre was established and run in ways antithetical to its success. The book poses the following questions: after the eighteen-year hiatus, why was London theatre reborn as a system founded on scarcity and luxury, rather than heterogeneity and accessibility? Why did those in charge make the decisions they did and then retain those approaches despite diminishing returns? And what might have happened if things were done differently? In short, why did such a critically acclaimed era for theatre experience such commercial struggles? Payne provides satisfying answers backed up by a wealth of evidence, resulting in a book that is essential reading for all working in the field.

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)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0170.007

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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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
Published2025
Admission routes1
Has abstractno

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