The “Silent” Shrew: The Taming of the Shrew in Text, Adaptation, and Performance
Bibliographic record
Abstract
This dissertation writes a new performance history of Shakespeare’s The Taming of the Shrew that considers the play in a range of social, political, and historical contexts. I analyze the play’s texts, adaptations, and performances through theories of adaptation, while also deploying abject theory in response to my observation that, in order to insist on its real-world relevance in challenging injustice, the play has staged deeply unsettling epistemic and systemic violence as a way of mobilizing audiences to take action. My methodology also involves theories of the anecdote: Shrew actors have been received as enacting their notorious lives on stage, bringing the play to life, and giving it real-world efficacy. The first of this project’s four chapters addresses Shrew and its early responses, showing how nascent intertextualities in the discourse of Shrew and non-dramatic texts of the seventeenth-century gender debate resonated across modes in a way that I argue brought the debate to life. Chapter 2 explores how Katherine has been a defining role for the actress; I contend that Katherine-focused Shrews depended upon an idea that the actress was enacting her real persona, and that this made the role into a star-vehicle. Chapter 3 focuses on notorious and married couples who have played Katherine and Petruchio; I argue that anecdotes connected to leading actors were deliberately fashioned in service of transforming the play into a story about actors’ offstage relationships. The final chapter discusses how violence and the abject have been staged in radical adaptations, contending that these productions implicated audiences and demanded they acknowledge their privilege and reconsider their contributions to unequal and harmful power systems. My conclusion uses a production of an all-female Shrew I directed in 2013 in Saudi Arabia in order to reflect on what my performance history has demonstrated about Shakespeare’s play: Shrew has worked in the world as a reflection of real relationships and actual domestic violence, in turn functioning as an agent for challenging oppressive systemic structures. When audiences leave the theatre believing they have witnessed real violence, Shrew gains pressing real-world relevance, with audiences compelled to actively participate in social change.
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 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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".