Dramaturgies of Emergence: Decentralized Collaboration of Participatory Theatre
Bibliographic record
Abstract
adrienne maree brown (Emergent Strategy) says that “relationships are everything.” The recent popularity of participatory theatre shows in Canada demonstrates just how true that is. These performances make direct audience interaction an essential feature of their dramaturgical design, with audience actions fundamental to propelling the performance experience. As these dramaturgies proliferate, they have given rise to a specific form of participatory theatre focused on decentralized collaboration and democratic audience agency, which I call dramaturgies of emergence. In emergent participatory performances, relationships manifest as networks of people – audience and artists – working together in a decentralized mode of iterative collaboration. With algorithmic dramaturgies to guide them, this autopoietic participant network generates the aesthetic and experiential aspects of the performance, and distinguishes emergent dramaturgies from other forms. By distributing the act of generating performance, it decentralizes agency over that creative process, distributing it throughout the networked participants to create outcomes no single member could generate alone. These dramaturgies of emergence embrace both relational modes of the established scientific process of emergence, and the political values of decentralization, playfulness, adaptability and collective leadership associated with the form by brown and others. By embracing these processes and values as their core mode of making meaning in a social setting, dramaturgies of emergence embrace the fundamental relationality of theatre, and ask us to playfully engage in those relationships. They allow audiences to authentically represent themselves in art while still creating something that is fundamentally collective, and provide a democratic mode of collaborative creation much needed in the often highly individualistic society we find ourselves in today. Works Cited Aarseth, Espen J. Cybertext: Perspectives on Ergodic Literature. Baltimore: John Hopkins University Press, 1997. Apter, Michael J. Danger: Our Quest for Excitement. Oxford: Oneworld Publications, 2007. Bennett, Jane. Vibrant Matter: A Political Ecology of Things. Durham: Duke University Press, 2010. brown, adrienne maree. Emergent Strategy: Shaping Change, Changing Worlds. Chico: AK Press, 2017. Fischer-Lichte, Erika. The Transformative Power of Performance: A New Aesthetics. Translated by Saskya Iris Jain. Abingdon: Routledge, 2008. Kuling, Peter. “Tilted Dramaturgy: Combined Spectatorship, Playwriting and Role-Playing in Bully-Pulpit’s Fiasco.” Canadian Theatre Review 178 (Spring 2019): 44-47. DOI: 10.3138/ctr.178.008. Latour, Bruno. Reassembling the Social: An Introduction to Actor Network Theory. Oxford: Oxford University Press, 2005. “Lost Together.” Unspun Theatre. Updated 2021. http://www.unspuntheatre.com/index#/lost-together. “Necessary Dream.” Up In the Air Theatre. Updated May 2021. https://www.upintheairtheatre.com/necessary-dream. Owen, David. “Performing the Game: Demystifying Live-Action Role-Play.” Canadian Theatre Review 178 (Spring 2019): 32-37. DOI: 10.3138/ctr.178.006. “Roll Models.” Play/PLAY: Dramaturgies of Participation. Updated September 2021. https://www.dramaturgiesofparticipation.com/roll-models.html. Salen, Katie and Eric Zimmerman. Rules of Play: Game Design Fundamentals. Cambridge: MIT Press, 2004. Sicart, Miguel. Play Matters. Cambridge: MIT Press, 2014. “Worktable.” SPIN. Updated July 2021. https://spinspin.be/kate-mcintosh/worktable/.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 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".