Performing “Acts”: The Entwined Lives Of Ballet Costumes And People Through Social Action
Bibliographic record
Abstract
Much like a fairy godmother transforming Cinderella for the ball, the role of a ballet costume completes a similar task in dressing a dancer into a character for a spectacle performance. However, there is more to a costume’s “life” than being worn onstage each night in front of an audience. A theatre costume is a vibrant object with animacy that, like humans, can live a long life filled with social interactions on and off the stage with different people throughout time. By analyzing two costumes from the National Ballet of Canada’s production of The Sleeping Beauty (1972), this study uses methodologies such as object-based research and object biography to trace the lives of these costumes to examine the different types of actions that it produces, as well as the human action produced by individuals to the costume itself. From the acts of making, wearing, and repairing costume, to the acts of archiving and researching, each phase of the garment’s life is filled with various types of people and actions that further create memory both in the costume’s materiality and an individual’s life. Working within the scope of new materialism and material culture that centres around the idea that objects and humans give meaning to one another, this paper discusses the entwined biographies of ballet costumes and the people involved throughout its lifetime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.010 |
| 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".