Sticking choices in timpani sight-reading performance
Bibliographic record
Abstract
When sight-reading a score, a timpanist needs to decide in real-time which stick to use to play a specific note while interpreting the musical material. Our main point of inquiry seeks to understand which sticking patterns performers employ and how they are affected by rhythmic stability. This paper analyzes the bi-manual sequencing (i.e., sticking) patterns of 31 timpanists in a sight-reading task. We analyze their results compared to model sticking patterns common in percussion pedagogical literature. Results show that while hand dominance plays an essential role in an individual's sticking pattern, the stability of a rhythmic pattern may also dramatically influence the observed particular sticking strategies. In areas of rhythmic stability, performers largely adhered to one of two conventional sticking patterns in the literature (dominant hand lead & alternating). Where rhythmic patterns became more unstable, the performers separated into diverse sticking groups. Moreover, several performers demonstrated sticking patterns which were hybrids or an inverse of the model sticking patterns, without any impact on the success of their sight-reading abilities. Overall, no two individual performers demonstrated the same sticking pattern. In terms of percussion pedagogy, our findings suggest that performers may benefit from an awareness of the adaptability of model sticking strategies. Lastly, we make the case for further study of rhythmic stability and bi-manual sequencing by locating the difference between notational and aural complexity.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".