Effects of Expertise and Auditory Guidance on Traditional Dance Performance
Bibliographic record
Abstract
This study investigated how the dancer's level of expertise and the type of auditory guidance provided influence the kinematic profile of the lower limbs during traditional dance performance. Ten experts in traditional Greek dance (age: 25±3.29 years, five males and five females) and eleven novice participants (age: 26.45±3.88 years, six males and five females), all Greek natives, performed a series of Greek and Irish dance steps with auditory guidance of the metrics (verbal counting) and the music of the respective dances. An electromagnetic tracking system sampled (at 100 Hz) the angular displacement of the two lower legs about the Mediolateral axis during dance performance. Segment rotations were analyzed in the time and frequency domain. Expert dancers displayed significantly lower variability of lower leg rotation and stronger interlimb coupling when compared to novice performers. In novice performers, the power of the lower limb angular displacement extended to higher frequencies when dance performance was guided by music compared to metrical guidance. The addition of music and the origin of the dance interfered with performance for novices but not experienced dancers. Kinematic analysis of the lower limbs may open a new window for the investigation of learning and auditory guidance effects on dance performance.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".