Polychronic Actants: Modern Promptbooks as Anticipated Acts, Unanticipated Acts, and Ideal Assemblages
Bibliographic record
Abstract
Modern Shakespeare promptbooks do not fit comfortably into any of the conceptual models current in discourses around the role of text in performance. Promptbooks operate as cue lists; records of unexpected acts; and records of or efforts to approximate ideal enactments. While promptbooks are not necessarily limited to these three temporalities, their encapsulation of all three points to their polychronic resistance to a straightforward and easily codified archival record of performance. This article presents a new theoretical model of promptbooks as temporal actants that are neither text nor performance in the ways currently understood in textual studies. The promptbook is more usefully conceived of as an actant within different theatrical networks at different times in production processes. To make this claim, the authors first revisit previous criticism on as well as misunderstandings of the purposes of Shakespearean promptbooks before theorising how a promptbook operates in relation to the larger event that is a theatrical performance. The article uses the 2005 promptbook of The Tempest from the Canadian Stratford Festival Archives as a case study to illustrate the ways in which promptbooks initiate the three kinds of temporal action the authors theorise: anticipated acts, unanticipated acts, and idealised polychronic assemblages.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".