From Clowns to Computers: Performing Theatrical Interactivity and Pervasive Transmedia Fictions
Bibliographic record
Abstract
The Collins English Dictionary defines “Interaction” as “a mutual or reciprocal action or influence”, and “Interactivity” as “allowing or relating to continuous two-way transfer of information between a user and the central point of a communication system”. \n \nThis study will analyze the range of pre-existing interactive theatre types, using the model of interaction theorized by Gary Izzo in The Art of Play. This model will be used to categorize and problematize the various strategies developed and deployed through seven years of practical interactive research in the theatre. The sites of this research include five productions I worked on as a director, from 2008-2012, with Toronto-based U.N.I.T. Productions, featuring clown duo Morro and Jasp, and an eight-month long, massive, trans- media fiction project called ZED.TO, created by The Mission Business, a local event design company where I worked in 2012 as both writer and narrative designer. \n \nThe central research question steering this dissertation is twofold. First, what strategies of interactivity already exist and how has the pre-existing theory of audience interaction behind these strategies evolved through the production and performance of these two projects? Second, in what ways have these strategies been proven effective, in real-time or during online encounters, to encourage an audience to believe, trust, share, play and ultimately participate inside an interactive theatre production? To prove the efficacy of these strategies, observations and opinions of both the public and the press are examined. \n\t \nThe answers to these research questions trace the sources, evolution and distribution of these strategies from within the established theatre practice (including improvisation and clown) as well as interactive approaches sourced from game design and social media. This multidisciplinary research helps to define what strategies work towards achieving interactivity in the theatre and how, or when, it is appropriate to utilize it during a theatrical production. \n \nIn essence, this study examines, through a survey of the history of immersive and interactive theatre, the strategies realized by the Morro and Jasp clown series and ZED.TO and how these projects have contributed to the evolving theory and practice of interactivity in the theatre. Analyzing such strategies will create a sourcebook for those seeking to bring theatre into the digital world as well as understand (and perhaps even undertake) the performance of pervasive interactive narratives in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".