“[So] What Do Dancers Know, [Anyway]?”: Voicing the Dancer’s Perspective through Emergent Choreographic Analysis
Bibliographic record
Abstract
[So] what do dancers know, [anyway]? Part manifesto, part essay, this article presents a woven, multi-level text discussion of my dancer/scholar standpoint and practice-as-research approach to articulating the dancer’s perspective. My recent research develops an emergent choreographic analysis of iconic American dance artist Deborah Hay’s choreography and practice, from my first-person experience as practitioner/performer in her solo “At Once” (2009). Writing as closely as possible to the dancing experience from within the practice, and using phenomenological, critical-poetic and performative writing strategies, this approach involves an iterative practicing, performing, and writing process that echoes the principles of adaptation underlying Hay’s own project. Reflecting Robin Nelson’s proposal that, in PaR, theory is imbricated within practice but must be articulated in complementary writing, I draw on Linda Hutcheon’s definition and principles of adaptation as a tool to amplify the resonances between Hay’s work, my artistic research itself, and the framework for its articulation. I ultimately position my research and writing not as a separate analytical reflection but as a critical-creative adaptation of Hay’s choreographic work.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".