Out at School: Imagining a Slow Ethic of Care in Research-Based Theater
Bibliographic record
Abstract
This article invites us to reconsider how we engage in ethical tensions and decision-making with the stories we are gifted as artist-researchers. Using a verbatim theater piece titled Out at School, we explore three moments of discomfort and growth that moved our collective approach toward a slow ethic of care. Within three ethical moments of dissonance, we investigate how to navigate a slow ethic of care in a project that is iterative and constantly shifting within and against our social and political world. By moving away from the desire for resolution, we argue for a process that understands the need to sit within ethical tensions as a way to commit to an ongoing slow ethic of care. We discuss our process, production, and performance as an invitation to critically reflect on ethical practices in research-based theater and reimagine ways to call in and move forward.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.066 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".