When the Audience is Ourselves: From Intellectual Argument to Visceral Experience
Bibliographic record
Abstract
This article reflects on a critical moment within a graduate course on using theatre for transformation. The course married critical readings and discussions on community and change-focused theatre with extensive simulations and other experiential opportunities in leading, creating and experiencing participant-based, activist theatre. One of these events, which engaged students with creating and performing participatory theatre, shook us up and led to some new understandings of how the use of theatre to study theatre can creep up and wallop one, making “education” highly personal, politicized and urgent. This event leads to a discussion of the values of personalizing education and creating “disturbance” as part of educational processes in all theatre classrooms. There are ethical considerations to reflect on, which challenge instructors and students, and possible implications for theatre classrooms that focus on acting and directing are considered.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.113 |
| Scholarly communication | 0.030 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| 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".