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Record W4402479435 · doi:10.3998/conversations.5961

Ethical Relationality in Dance Rehearsal Spaces: A Case Study of <i>Mizuki No Eki</i>

2024· article· en· W4402479435 on OpenAlexaff
Peter Farbridge, Melanie I. Stuckey

Bibliographic record

VenueConversations Across the Field of Dance Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsNational Circus School
Fundersnot available
KeywordsDancePhilosophyArtCommunicationSociologyVisual arts

Abstract

fetched live from OpenAlex

Performing arts are important to the cultural fabric, yet in North America, they tend to lack diversity and may reinforce social inequities through training and performance that is based on hegemonic cultural identities. In this paper, we explore an approach to creation and rehearsal that could be more inclusive to diverse perspectives and identities such that performing arts can convey the complexities of multiple realities relevant to the North American context. We propose ethical relationality, a conscious consideration of relationships with the self, others, historical legacies and social and physical environments, as an approach to the creation and rehearsal processes. The application of ethics to these processes has the potential to produce more relatable, pluralist and ethical creation practices and performances and promote responsible, responsive, and affective engagement with others. It requires a flexible, collaborative approach with shared power and shared responsibility for each other, the engagement of all participants in the process, and the work created. Through a case study of a wordless theatrical dance performance, we give a concrete example of how an ethical relationality approach could facilitate open and courageous creative processes, acknowledging the various factors of oppression and omission to which the performing arts are subject.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.169
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.383
Teacher spread0.343 · 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
Published2024
Admission routes1
Has abstractyes

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