Distributed Agencies in Dramatic Form: A Posthuman Perspective on Lucy Prebble’s <i>The Sugar Syndrome</i> and Sarah Ruhl’s <i>Dead Man’s Cell Phone</i>
Bibliographic record
Abstract
The past two decades have seen a significant increase in western drama incorporating digital technologies on stage. While theatre scholars have regularly applied posthuman or cyborg theory to make sense of digital spectacle in performance, this article extends a posthuman approach to dramatic form by considering two plays from the early 2000s, a time of substantial technological change within affluent western societies. In both Lucy Prebble’s The Sugar Syndrome (2003) and Sarah Ruhl’s Dead Man’s Cell Phone (2007), the human characters share dramatic agency with the digital devices that surround them, co-initiating and co-escalating the drama. This distributed agency creates a shift in how the humans begin to perceive themselves and each other: from rational, coherent, autonomous selves – a liberal humanist subjectivity – to heterogeneous assemblages reminiscent of a digital computer – a posthuman subjectivity. While much posthuman theatre scholarship has focused on digital or bodily spectacle, dramaturgical analysis can also reveal the neglected technology of dramatic form to construct posthuman subjectivities for the stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".