Bibliographic record
Abstract
All forms of dance incorporate examples of the human body in motion or standing in an aesthetically appealing pose. While balancing in a static position, dancers make small adjustments to avoid toppling. Balancing when the body is rotating around a vertical axis with one supporting foot at the floor (a pirouette) is more complex. Dancers are often taught to perform pirouettes by beginning the movement as close to balanced as possible and holding the body rigid throughout the turn. Theoretically, if the body is initially perfectly balanced, the number of revolutions will be limited only by friction. However, the research reported here demonstrates that dancers who perform pirouettes without making adjustments to correct for loss of balance will likely be unable to complete more than a two or three turn pirouette. The topple angle (θ) as function of time was predicted for a model of a rigid body dancer. When θ becomes large enough, dancers must compensate for loss of balance or end the turn. That limiting angle was determined experimentally and set as the upper limit to θ. It was found that performing more than a three-turn, rigid-body pirouette requires the dancer to begin unreasonably close to perfectly balanced (θ<0.1°). To successfully perform more than a three turn pirouette, dancers must employ one of three strategies for regaining balance: (1) Return the center of mass (CM) to a location vertically above the supporting foot (SF), which requires a horizontal force on the body from the floor; (2) Hop the SF to a position vertically under the CM, which compromises the aesthetic of the movement; or (3) slide the SF under the CM, which moves the area of support and produces a horizontal force to move the CM to a revised location. An experimental analysis of dancers’ pirouettes was performed in order to determine what adjustment mechanisms were successfully employed to regain balance. Sliding the SF was the most common and effective technique used by advanced dancers. The SF slides were accompanied by a decrease in normal force, so the instruction to “lift” when performing a pirouette may be useful.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".