Textual Matters: Making Narrative and Kinesthetic Sense of Crystal Pite's Dance-Theater
Bibliographic record
Abstract
In this article, I examine the work created by Canadian choreographer Crystal Pite for her company Kidd Pivot, placing it within a larger tradition of dance-theater that combines text and movement, and traffics openly in big emotions and even bigger narrative structures. I argue that Pite's use of text does not just offer a way into, or a representational gloss on, her otherwise abstract movement vocabulary, but also a means of affectively and even kinesthetically re-experiencing that movement post-performance. I focus on Pite's three most recent evening-length programs for Kidd Pivot, and on the different modes of textual address employed therein (voice-over narration, projection, live speech). My goal in analyzing the choreographer's narrative scripts alongside her physical ones is to highlight, on the one hand, the materiality of words within the total sensory environments created by Pite through her dance-theater performances and, on the other, to emphasize their consequentiality in helping to make somatic sense of one's memories of those performances.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".