Bibliographic record
Abstract
Applied Theatre: Ethics explores what it means for applied theatre practice to be conducted in an ethical way and examines how this affects the work done with communities and participants. It considers how practitioners can balance aesthetics and ethics when creating performance, particularly with relatively inexperienced and often vulnerable groups of people who are being asked to both tell and stage their stories. The two sections bring together theoretical and practical ways for theatre-makers to examine the ethics of their applied theatre projects. Part One offers an overview of critical debates and the editors’ reflections on their own practice. It introduces readers to ethics in applied theatre, informed by the thinking of philosophers, scholarly literature and the editors’ own experience, including Indigenous perspectives on ethics and theatre. For applied theatre practitioners, it provides recommendations for community-based ethical approaches working with principles of voice, agency, care, service, collaboration, presence, relationality and reciprocity. Part Two presents a range of international case studies that explore how the theories and issues are worked out in a variety of diverse practices. It considers ethics from varying critical perspectives and contexts, including projects in Greece, Nigeria, Sri Lanka, Bangladesh, the United States, the United Kingdom, the Philippines and Canada. Covering work with participants of many ages, the case studies include the work of a professional dance theatre company working with people in substance abuse recovery in the UK, interactive drama used in an educational context in Nigeria, and the complexities around an applied theatre project on race in the US.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.048 | 0.024 |
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".