SHAKESPEARE'S GREATEST RIVAL: An Exploration of Early Modern Writing Practice Through an Original Play
Bibliographic record
Abstract
My project was created in response to the work in Early Modern Staging Practices (EMSP) occurring at Shakespeare’s Globe in London and The American Shakespeare Center (ASC) in Staunton, Virginia. Having been a former actor at ASC, who has embodied many of the conventions and conditions of this style and participated in Practice as Research (PaR) projects, it dawned on me that most of the scholarship undertaken at these venues is back-ended in its approach. The study is often about the final processes of staged theatrical activity. However, if we want to get a complete picture of EMSP, then surely a writing practice must be explored to see what kinds of strategies and tactics cohere with Early Modern conditions and conventions. Utilizing a methodological approach that leans heavily into PaR, I have coined the term ‘Writing Toward Physical Practice’ (WTPP) to specify a type of creative writing that imagines and physicalizes future theatre embodiment and blocking in this particular style. In addition, I have leaned heavily into autoethnography as I believe my experience as an actor may illuminate staging conditions and conventions through the exercise of writing. My new play, Shakespeare’s Greatest Rival, is the byproduct of this writing exploration. The work explores mercurial and enigmatic playwright Christopher Marlowe. It tracks his atheism, his same-sex attraction, and his employment as a spy while he competes against William Shakespeare as England’s greatest playwright. As an approach to organization for this project, I have listed a set of conditions and conventions, which I have examined while writing Shakespeare’s Greatest Rival. They are: 1. Recycling Structure, Plot and Narrative Devices. 2. Embedded Stage Direction. 3. Songs 4. The Great Chain of Being. 5. Anachronism. 6. Shared Lighting. 7. Meta-Theatricality. Many of these categories have sub-categories to be examined, but I hope that mentioning these categories now may help with a thorough and streamlined process of study.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.040 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".