Site, Adapt, Perform: A Practice-as-Research Confrontation with Climate Change
Bibliographic record
Abstract
In recent years, arts festivals around the globe have become enamoured of touring, site-based performance. Such serialised site work is growing in popularity due to its accessibility, its spectacular characteristics, and its adaptive qualities. Employing practice-as-research methodologies to dissect the basis of such site-adaptive performances, the author highlights her discovery of the crumbling foundation of the adaptation discourse by way of her creative process for the performance work Room. Combining findings from the phenomenological explorations of her dancing body as well as from cultural analyses of the climate change debate by Dipesh Chakrabarty (2009), Claire Colebrook (2011, 2012), and Bruno Latour (2014), the author argues that only by fundamentally shifting the direction of the adaptation discourse – on scales from global to the personal – will we be able to build a site-adaptive performance strategy that resists the neoliberal drive towards ecological and economic precarity.
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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 | 0.050 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.130 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".