Meeting Trees Halfway: Environmental Encounters in Theatre and Performance
Bibliographic record
Abstract
abstract: How do trees (live and representational) participate in our theatrical and performed encounters with them? If trees are not inherently scenic, as their treatment in language and on stage might reinforce, how can they be retheorized as agents and participants in dramatic encounters? Using Diana Taylor’s theory of scenario to understand embodied encounters, I propose an alternative approach to understanding environmental beings (like trees) called “synercentrism,” which takes as its central tenet the active, if not 100 percent “willed,” participation of both human and non-human beings. I begin by mapping a continuum from objecthood to agenthood to trace the different ways that plants and trees are used, represented, and included in our encounters. The continuum provides a framework that more comprehensively unpacks human-plant relationships. \n\nMy dissertation addresses the rich variety of representations and embodiments by focusing on three central chapter topics: the history of tree representation and inclusion in dramatic literature and performance; interactions with living trees in gardens, parks, and other dramatic arenas; and individual plays and plants that have a particularly strong grasp on cultural imaginaries. Each chapter is followed by one or more corresponding case studies (the first chapter is followed by case studies on plants in musical theatre; the second on performing plants and collaborative performance events; and the last on the dance drama Memory Rings and the Methuselah tree). I conclude with a discussion of how the framework of synercentrism can aid in the disruption of terministic screens and facilitate reciprocal relationships with trees and other environmental agents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| 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".