Unraveling mysteries of personal performance style; biomechanics of left-hand position changes (shifting) in violin performance
Bibliographic record
Abstract
Instrumental music performance ranks among the most complex of learned human behaviors, requiring development of highly nuanced powers of sensory and neural discrimination, intricate motor skills, and adaptive abilities in a temporal activity. Teaching, learning and performing on the violin generally occur within musico-cultural parameters most often transmitted through aural traditions that include both verbal instruction and performance modeling. In most parts of the world, violin is taught in a manner virtually indistinguishable from that used 200 years ago. The current study uses methods from movement science to examine the "how" and "what" of left-hand position changes (shifting), a movement skill essential during violin performance. In doing so, it begins a discussion of artistic individualization in terms of anthropometry, the performer-instrument interface, and the strategic use of motor behaviors. Results based on 540 shifting samples, a case series of 6 professional-level violinists, showed that some elements of the skill were individualized in surprising ways while others were explainable by anthropometry, ergonomics and entrainment. Remarkably, results demonstrated each violinist to have developed an individualized pacing for shifts, a feature that should influence timing effects and prove foundational to aesthetic outcomes during performance. Such results underpin the potential for scientific methodologies to unravel mysteries of performance that are associated with a performer's personal artistic style.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".