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Record W2585197163 · doi:10.3138/ctr.169.003

Off-Script: A Formulaic “Freedom” in Rhythmic Gymnastics’ Code of Points

2017· article· en· W2585197163 on OpenAlexvenueno aff
Christine Mazumdar

Bibliographic record

VenueCanadian Theatre Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsRhythmic gymnasticsChoreographyEliteVisual artsFace (sociological concept)Set (abstract data type)Competition (biology)Code (set theory)Movement (music)AestheticsDanceArtComputer sciencePsychologySociologyMathematics educationLawPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Rhythmic gymnastics is an aesthetic sport predicated on the interrelationship of technical virtuosity and artistic prowess. In accordance with the 2013–2016 rules of the Fédération Internationale de Gymnastique (International Gymnastics Federation; FIG), rhythmic gymnastics choreography is predicated on the “unity” of this technical-artistic binary; however, the technical and artistic qualities of the choreography are evaluated by two separate judging panels. As such, the development of an elite-level gymnast must consider not only athletic conditioning but also artistic development—becoming an athlete and an artist. The complications and contradictions that arise from trying to quantify the technical (objective) and the artistic (subjective) performed within competitive routines, I argue, is foregrounded by the FIG’s 2001 incorporation of the gymnast’s “difficulty script,” which requires each gymnast to supply a copy of their planned elements to the judges before the competition. Exploring three different case studies from international tournaments, I consider not only how choreographers are limited in their artistic freedom by having to incorporate a set list of compulsory elements into each routine but also how, in competition, performers face the added challenge of preserving the semblance of spontaneity in a routine that is being simultaneously read by the judges.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.318
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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