Off-Script: A Formulaic “Freedom” in Rhythmic Gymnastics’ Code of Points
Bibliographic record
Abstract
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.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".