Reflections on Teaching Process Drama: A Critical Inquiry into Our Practice with/as Educators
Bibliographic record
Abstract
This paper explores our experiences as drama-in-education professors teaching educators how to create and facilitate process dramas (Bolton & Heathcote, 1995; Neelands & Goode, 2000; O'Neill, 1995) in their classrooms. A process drama involves multimodal embodied drama explorations covering a specific topic that the facilitator(s) would like the participants to explore. In this narrative of practice, we present an example of process drama that our university students created and facilitated that broached critical topics surrounding social justice for their students to explore. The university students ranged from teacher candidates in a Bachelor of Education program to those with many years of experience completing a Master of Education. In our experiences teaching drama-in-education courses, we have encountered ethical dilemmas in the creation and facilitation of process dramas. Specifically, in the topics our students have selected and their positionalities as facilitators. In this article, through narrating our teaching experiences and what we learned from them, our goal is to call for artists and educators, like ourselves, to be more thoughtful in approaching the creation and facilitation of process dramas, especially when teaching people with different subjectivities and positionalities.
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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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.067 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".