Global Networked Ecoscenography: Creating Sustainable Worlds for Theatre Through International Collaboration
Bibliographic record
Abstract
Historian Yuval Harari refers to civilization as a fiction we agree to. Conversely, we can look at “fiction,” “narrative” or even performance” as an effort to (re)create and redesign civilization. In Performance and Ecology: What can Theatre Do? Carl Lavery highlights the ecological potential in the work of Karen Christopher and Sophie Grodin, which requires artists to be open to each other and more-than-human materials. We attempt to extend this approach through a Global Networked Learning initiative between York University (Canada), Griffith University (Australia) and Queensland University of Technology (Australia) which aims to train emerging ecoscenographers to explore more-than-human collaborations across vast distances. This visual essay documents the process and outcome of the design student responses to the plays of the 2021 Climate Change Theatre Action (CCTA) project, in partnership with a professional global EcoDesign Charrette focused on the 50 plays of the CCTA. The remote studio setting is structured as a series of collaborative workshops on EcoScenography, which invite the students to create seed design concept for exhibition at the World Stage Design Festival in Calgary in August 2022. As ecoscenographers, we are deeply committed to reducing the environmental harm of our field, both in academic and professional settings, including the creation of infrastructure to leverage the benefits of international cooperation without the significant impacts of travel. This is true both as an accessible opportunity for students and as an example to the global performance field to consider distance collaboration, particularly as it connects to touring and arts related travel. These are significant considerations regarding ecological impacts, costs and accessibility, as well as the more immediate considerations of the COVID-19 pandemic where Canada’s borders are limited to essential travel and Australia is not allowing travel in or out of the country while day-to-day life has returned to an almost pre-pandemic mode of social interaction. This course is an attempt to overcome these challenges to share our climate of attention, first with one another and then with the global design community.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".