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Record W4307423004 · doi:10.25071/28169344.13

Reimagining consensual engagement in drama education: the possibilities of intimacy choreography in a “post”-COVID-19 world

2022· article· en· W4307423004 on OpenAlexaff
Kristy Smith

Bibliographic record

VenueYU-WRITE Journal of Graduate Student Research in Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsYork University
Fundersnot available
KeywordsDramaContext (archaeology)ChoreographyApprenticeshipCoronavirus disease 2019 (COVID-19)PsychologySociologyScholarshipPedagogyDancePolitical scienceVisual artsMedicineLawArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.240
GPT teacher head0.467
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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