Bibliographic record
Abstract
This dissertation argues that the notebooks and scripts belonging to directors and actors, stage managers’ scripts, and other documents of working theatre are the materials that make performances of Shakespeare. While literary-minded editors focus on Shakespeare’s print history and performance theorists operate metaphorically, reading entire productions as texts, these little-seen backstage documents comprise theatre’s textual history, the only written history of performance as it is created. This is an immense amount of material that is currently missing from academic investigation, critical consideration, and potential use in the classroom. I assert that it has untapped potential for two areas in particular: pedagogy and editing.Typically, editors guide readers through Shakespeare’s text in part by helping them to imagine it being staged. I believe the same aim might be achieved—more effectively—by incorporating the work of practitioners into scholarly editions. Scholars including Barbara Hodgdon, Peter Holland, M. J. Kidnie, and W. B. Worthen have incisively critiqued traditional editing practices as anti-theatrical, especially against modern performance; they have sought but—lacking extensive experience with and reliable access to backstage texts—have not conclusively found ways that editing might better capture modern performance. My thesis is that an editor should have a few specific, recent productions in mind when producing an edition of a play and that they should use those performances and the working backstage texts that created them as an explicitly stated basis of their text, incorporating the notes of directors and actors in a paratextual performance apparatus akin to bibliography’s collation band. Part I of my thesis, “Backstage Texts,” lays out the archival evidence and trains readers to identify the ownership and the use of a script, while Part II, “The Editing of Shakespeare,” critiques current editorial approaches and examines how backstage texts may be brought into the editorial project in order to forge closer collaboration between editors of early modern drama and theatre practitioners. This is a completely new kind of editorial grammar, so in my final chapter I offer my own annotative practice to illustrate how this new integration—page by way of stage— might be achieved.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".