Ethical Relationality in Dance Rehearsal Spaces: A Case Study of <i>Mizuki No Eki</i>
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".