Biomechanical Research in Dance: A Literature Review
Bibliographic record
Abstract
The authors reviewed the literature, published from 1970 through December 2009, on biomechanical research in dance. To identify articles, the authors used search engines, including PubMed and Web of Science, five previous review articles, the Dance Medicine and Science Bibliography, and reference lists of theses, dissertations, and articles being reviewed. Any dance research articles (English language) involving the use of electromyography, forceplates, motion analysis using photography, cinematography or videography, and/or physics analysis were included. A total of 89 papers, theses/dissertations, and abstracts were identified and reviewed, grouped by the movement concept or specialized movements being studied: alignment (n = 8), plié (8), relevé (8), passé (3), degagé (3), développé (7), rond de jambe (3), grand battement (4), arm movements (1), forward stepping (3), turns (6), elevation work (28), falls (1), and dance-specific motor strategies (6). Several recurring themes emerged from these studies: that elite dancers demonstrate different and superior motor strategies than novices or nondancers; that dancers perform differently when using a barre as opposed to without a barre, both in terms of muscle activation patterns and weight shift strategies; that while skilled dancers tend to be more consistent across multiple trials of a task, considerable variability is seen among participants, even when matched for background, years of training, body type, and other variables; and that dance teachers recommend methods of achieving movement skills that are inconsistent with optimal biomechanical function, as well as inconsistent with strategies employed by elite dancers. Measurement tools and the efficacy of study methodologies are also discussed.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.034 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".