DEBORAH C. PAYNE. <i>The Business of English Restoration Theatre, 1660–1700</i>
Bibliographic record
Abstract
‘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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".