Bibliographic record
Abstract
Performing musicians frequently use physical gestures that are more elaborate than required for sound production alone. Such movements are not prescribed in traditional musical scores, nor are they evident in audio recordings, and consequently they are rarely regarded as integral to a formal musical analysis. However, there is growing evidence that these movements do in fact alter an audience’s listening experience—i.e., the way a performance “sounds.” Therefore, we believe that analyses of these movements can inform more traditional analyses of notes and rhythms by lending insight into the way in which these musical elements areperceived. Here, we review research on the role of gestures in shaping the musical experience, focusing in particular on gestures used by percussionists to control perceived note duration. This paper embraces the multi-media affordances ofMusic Theory Onlineby integrating stimuli from key experiments—the first publication of these materials. Our aim is not only to summarize a growing body of work on the musical role of extra-acoustic factors such as ancillary gestures, but also to present new avenues of musical research that complement existing approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".