Bibliographic record
Abstract
How did Shakespeare write his plays and how were they revised during their passage to the stage? James Purkis answers these questions through a fresh examination of often overlooked evidence provided by manuscripts used in early modern playhouses. Considering collaboration and theatre practice, this book explores manuscript plays by Anthony Munday, Thomas Middleton, and Thomas Heywood to establish new accounts of theatrical revision that challenge formerly dominant ideas in Shakespearean textual studies. The volume also reappraises Shakespeare's supposed part in the Sir Thomas More manuscript by analysing the palaeographic, orthographic, and stylistic arguments for Shakespeare's authorship of three of the document's pages. Offering a new account of manuscript writing that avoids conventional narrative forms, Purkis argues for a Shakespeare fully participant in a manuscript's collaborative process, demanding a reconsideration of his dramatic canon. The book will greatly interest researchers and advanced students of Shakespeare studies, textual history, authorship studies and theatre historians.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
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".