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Record W4381326272 · doi:10.32920/23548236.v1

Performing “Acts”: The Entwined Lives Of Ballet Costumes And People Through Social Action

2023· preprint· en· W4381326272 on OpenAlexaffabout
Avalon Catherine Acaso

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBalletArtVisual artsSpectacleAestheticsObject (grammar)BeautyMateriality (auditing)Action (physics)DanceComputer science

Abstract

fetched live from OpenAlex

<p>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. </p>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.138
GPT teacher head0.299
Teacher spread0.162 · 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.

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
Published2023
Admission routes2
Has abstractyes

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