Ethical Implications of Using Research-Based Theater to Challenge Hegemonic Narratives About Mental Health
Bibliographic record
Abstract
Many health researchers have started to see the benefits of partnering with playwrights to use theater as a tool to analyze and present complex findings. Such projects about mental health, however, remain few and far between and are especially fraught. This article argues that research-based theater can be an ideal tool for exploring and sharing counter-hegemonic “mad stories”—especially if the plays created can help interrupt, among other things, problematic biomedical narratives about individualized approaches to supporting those who are suffering. This article also incorporates excerpts from the script the author wrote as part of her doctoral thesis project about military trauma to highlight how, guided by mad theory and mad aesthetics, she has creatively woven some of the weightiest ethical conundrums encountered during the research and development process into the play itself, so that, their nuance and magnitude become a critical component of the story being presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.068 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.112 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".