Can a Prescribed Turnout Conditioning Program Reduce the Differential between Passive and Active Turnout in Pre-professional Dancers?
Bibliographic record
Abstract
Preliminary and speculative findings are reported on the benefits of a prescribed turnout conditioning program (TCP) designed to facilitate pre-professional dancers' active use of natural turnout potential. While of some debate, it is reported in the literature that many dancers use less turnout than what is available to them when measured passively. Key muscles required to achieve full turnout were the focus of the TCP, and exercises were introduced in a manner that, theoretically, should stimulate appropriate activation patterns for proper turnout biomechanics. A group of female pre-professional dancers (13 to 17 years old, training 20 to 25 hours a week, N = 16) were measured before and after the 7-week program for total passive turnout, total active turnout, passive hip external rotation, and tibial torsion. Statistically and functionally significant improvements were found in both static total active turnout (standing in first position on a large piece of paper) and dynamic total active turnout (standing in first position on rotational Balanced Body discs). These results indicate that the TCP was effective in improving active turnout, thereby reducing the differential between passive and active turnout in pre-professional ballet dancers. Implications are discussed for dancer-specific turnout conditioning programs, the role of cognitive imagery cueing, and emphasis on the importance of quantity with quality in the conditioning and teaching of active turnout.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".