Digital Shakespeares and the Performance of Relevance
Bibliographic record
Abstract
abstract: “Digital Shakespeares” is a study of the ways that Shakespearean theaters and festivals are incorporating digital media into their marketing and performance practices at the beginning of the twenty-first century. The project integrates Shakespeare studies, performance studies, and digital media and internet studies to explore how digital media are integral to the practices of four North American and British Shakespearean performance institutions: the Oregon Shakespeare Festival, the Royal Shakespeare Company, Shakespeare’s Globe, and the Stratford (Canada) Festival. Through an analysis of their performance and marketing practices, I argue that digital media present an opportunity to reevaluate concepts of performance and relevance, and explore the implications such reevaluations have on the future of Shakespearean performance. The project addresses institutions’ digital media practices through the lens of four concepts—access, marketing, education, and performance—to conclude that theaters and festivals are finding it necessary to adopt practices from multiple media to stay viable in today’s online attention economy. The first chapter considers the issue of access, exploring the influence of social media on audience-institution interactions as theaters and festivals establish online presences on sites like Facebook, Twitter, and Pinterest. Chapter two argues that theaters and festivals incorporate digital media into their outreach through poaching the practices of other media and cultural institutions as they strive to become relevant to their online audiences by appealing through the newness of digital media. Chapter three focuses on two digital educational outreach programs, the Globe’s Playing Shakespeare and the RSC’s Young Shakespeare Nation, to understand how each institution seeks to employ digital media to make their educational audiences life-long lovers of Shakespearean performance. Throughout the final chapter, I analyze potential models for incorporating digital media into Shakespearean performance, both in performances that bring digital media onto the stage and in performances that use social media as the platform for dramatic performance. Ultimately, I argue digital media have become an integral part of the practices Shakespearean performance institutions use to establish and sustain their cultural relevance with modern audiences, while raising questions regarding the implications of those practices in an increasingly globalized world.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".