Staging Shakespeare in Social Games: Towards a Theory of Theatrical Game Design
Bibliographic record
Abstract
This essay discusses the theoretical implications of a recent experiment with game-based social media to increase Shakespeare literacy in eleven to fifteen-year-olds. In collaboration with the Stratford Festival, we aimed to make the gameplay of our pilot, Staging Shakespeare, and the social space it generated, experientially theatrical in some way. While the pilot itself was not, in our view, successful, the design process helped us articulate a theory of theatricality grounded in the ontological complexity of theatrical things and the ontogenetic conditions of theatrical environments. Our conclusion is that literal simulations of Shakespeare's plays or of Shakespearean theater production may not be the richest way to teach Shakespeare through social games. Instead, we may need a design theory grounded in the adaptation of theatrical principles to electronic media, and perhaps a new aesthetic and even a rhetoric of gameplay only associatively related to Shakespeare.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.044 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".