Reimagining consensual engagement in drama education: the possibilities of intimacy choreography in a “post”-COVID-19 world
Bibliographic record
Abstract
COVID-19 has brought forth new risks for students and teachers as they navigate how to engage safely with each other. It becomes necessary to consider the role of consent as a daily practice in “post”-pandemic life and explore what consent may offer young people as agents of their own bodies. In this paper, I consider how the emerging field of intimacy choreography (IC) illuminates new possibilities for engaging ethically with others. I situate this exploration in the context of drama education, guided by the following questions: how may IC provide practical tools for fostering consensual interactions amongst students, their peers, and their teachers? How may IC shed light on new ways of living more ethically with others? This paper discusses the potential of IC through the five pillars of rehearsal and performance practice identified by Intimacy Directors and Coordinators (Percy, 2020), supplemented by IC scholarship and professional literature (Ates, 2019; Lehmann, 2018; Morey, 2018; Pace, 2020; Purcell, 2018; Sina, 2014), and reflections on my experiences as a drama teacher working with an IC apprentice and high school students to share observations of how IC promoted consent in rehearsal. This paper will conclude with suggestions for how IC can help teachers support students in a “post”-COVID-19 context.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".