Through a Glass Brightly: Generative Ethical Tensions in Research-Based Theatre
Bibliographic record
Abstract
This qualitative case study methodically explores ethical tensions that arose in the Research-based Theatre (RbT) project, Alone in the Ring (AitR), as a case. We borrowed Elliot Eisner’s set of tensions in Arts-Based Research (ABR), exploring the extent to which they manifested as ethical tensions in AitR. Following analysis of in-depth interviews with key project members, we identified five areas of ethical tension in AitR, adapting Eisner’s framework to account for the ethical dimensions of the tensions, their generative quality, and their temporal and social dimensions, as they manifested in AitR. Complicating Eisner’s general tensions for ABR, this article advances an adapted, RbT-specific framework with meta-language to reflect on the ethical terrain of RbT using the richness and specificity afforded by a case study. The framework is particularly useful for RbT practitioners seeking to maximize the benefits of RbT for knowledge translation, arts-based inquiry, and community engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".