On Being a Walking Body: Dramaturgies of Participatory Pandemic Theatre
Bibliographic record
Abstract
Mariah Horner and Jacob Pittini are members of Dr. Jenn Stephenson’s research team investigating participatory dramaturgies in Canada. In May 2020, Pittini and Horner attended Zuppa Theatre Co’s Vista20, an ambulatory app-guided play about the COVID-19 pandemic. Through dramaturgical analysis, Pittini and Horner explore Vista20’s use of walking to situate participants in their embodied experiences of the ongoing pandemic, with a focus on liveness, duality, and digital participation across distances. The app-based experience encourages participants to explore their own contexts through walking while connecting them to storytellers in Halifax, revealing complex dramaturgies of together/apart. Pittini and Horner analyze how Vista20 uses technology to achieve a unique embodied yet socially distanced form of participation in response to the pandemic, representing an exciting form of theatrical liveness.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".