Resistance Breeds Revolution: A Study of Refusing the Invitation in Participatory Theatre
Bibliographic record
Abstract
To join in a participatory theatre experience, the audience needs to accept the invitation to cross the fourth-wall and become a player (White). But what happens when aspects of our participation are unethical, or add odds with our political or social values? How do artists prepare for a participant who is resistant to engagement? This essay will explore the concept of the invitation, the possibility that lies in saying “yes”, and the power that exists in refusing the offering. I consider a variety of different modes of saying no, differentiating the social and political meaning and impact of ‘refusal,’ ‘opting out,’ ‘going rogue,’ ‘being a killjoy,’ ‘non-participation,’ or simply an inability to participate. Accustomed to decades of proscenium theatre experiences, being a good audience has become synonymous with receiving performances in silence, at a static distance, and with applause (Heddon, Iball, and Zerihan). We are trained to suppress our questions and concerns to refrain from challenging the methods or intentions of the artists in front of us. I dissect various personas of refusal who reflect a show’s dramaturgical flaws, accessibility issues, unethical content, faulty or lack of clear invitations, or uninteresting material. But saying “no” is challenging, and I will investigate the tyranny of the invitation and the infrequency of refusal, highlighting the generative nature of resistance. This paper will consider the example of Jordan Tannahill’s mixed reality piece, Draw Me Close, which invites participants into an animated virtual reality space that requires participation, and it unpacks how unexpected acts of refusal are accounted for and dealt with by artists.
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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 | 0.039 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.050 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".