Ecoscenography in the global classroom: Creating sustainable worlds for theatre through international collaboration
Bibliographic record
Abstract
This presentation shares the process of and subsequent impact of embedding ecoscenographic thinking into the education of tertiary theatre students. As part of a Global Networked Learning initiative between York University (Canada), Griffith University (Australia) and Queensland University of Technology (Australia), twenty-five university students were trained in sustainable, ecologically conscious approaches to designing for live performance across 2021/22. Students were guided by professional designers and educators in scenography, sustainability and technology, working in partnership with the 2021 Climate Change Theatre Action (CCTA) project to produce seed design concepts for new climate plays 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 professional and academic settings. By encapsulating the “integration of an ecological ethic with performance design”, ecoscenography calls for “new approach to theatre production that overturns traditional production models” (Beer 2021 pp. 4, 18). Building on this call, our project sought to overturn traditional models of design training also by employing the three cornerstones of ecoscenographic practice—co-creation, celebration and circulation—in a higher education context. This presentation will detail our pedagogical approach while sharing the outcomes of adopting an ecological ethic within the training of the next generation of designers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".