A Landscape of Ethics in Research-Based Theater: Staging Lives of Family Members Who Have Passed
Bibliographic record
Abstract
Blending academic and theatrical worlds in research-based theater (RbT) requires balancing academic demands of institutionalized research and aesthetic demands of theater. This duality becomes particularly significant regarding ethics. With so much possibility, it becomes imperative for practitioners to share, and learn from, vulnerable and challenging ethical experiences. This creates landscapes of ethical possibilities for RbT and guideposts for navigating them, established by the field, highlighting well-worn paths, pointing out pitfalls, and noting where few have yet to trod. Contributing to this cartography, we consider ethical questions encountered during development and production of three RbT projects involving family members who have passed: Homa Bay Memories, Brothers, and Unload. In doing so, we question which stories might be best left untold and the evolution of relationships throughout the research. Exploring these together helps to develop a landscape of ethical possibilities and establish guideposts to help illuminate challenges for future RbT projects.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.056 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| 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".