Connecting Climate Change and Canadian Theatre: Reflecting on the Impact of the NAC’s Climate Cycle
Bibliographic record
Abstract
The arts have a unique way of affecting the way that people feel about environmental issues in ways that other forms of communication cannot. The National Arts Centre (NAC) Climate Cycle was a program that the NAC English Theatre developed as a response to the escalating climate crisis to engage artists deeply in discussions on climate change and to grapple with how the performing arts can respond. It took place over two meetings: the Summit in Banff in 2019, which was in person, and the Green Rooms in 2020, which was online. To answer our main question of how participating in these events impacted artists’ behaviour and professional practices, we engaged with the participants through a series of surveys and in-depth interviews. Based on the results, we found that artists were impacted in three main ways: (1) instilling a sense of community among the participants; (2) encouraging the participants to continue their learning; and (3) enacting strategies to ‘green’ their practices, such as reducing touring and using alternative materials in set design. This project was helpful in understanding how the Cycle impacted artists, but it also contributed to our overall understanding of what meaningful engagement with artists on environmental topics looks like. This project was first completed as part of an undergraduate thesis in May 2021. In this most recent article, we reflect on what this research means a year later.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".