Measuring the Impacts of Extra-Musical Elements in Guitar Music Playing: A Pilot Study
Bibliographic record
Abstract
Philosophers, composers, and musicians have long argued whether instrumental music finds meaning in its formal structure and musical content (Hanslick, 1986) or through reference to extra-musical elements, like narratives, emotions, or memories (Meyer, 1956). While the use of extra-musical elements appears grounded in individual musicians' priorities for performance and teaching (Héroux, 2018), the impact of emotional indications on expressivity has not previously been studied in a large-scale experiment. The aim of this pilot study was to construct the methodology for a larger project to study the impact of the use of extra-musical elements on the sound results of guitarists. We asked guitar students to record one short newly composed piece, Evocation 1, according to the following conditions: (A) in a non-expressive manner, (B) according to the notated musical indications, and (C) with the addition of suggested contextual and emotional extra-musical elements to the musical instructions. We asked two expert guitarists to evaluate the level of expressiveness for conditions B and C and conducted interviews with participants to collect data on the experimental process to refine protocol. To more objectively measure manifestations of objectivity from the recorded performances, we extracted data from each recording about pitch, dynamics, and timing, as well as expressive dynamic deviations. The impact of both recording conditions and the expertise level of performers on the quality of this audio data led us to change the analysis design from a comparative design (with other participants) to a self-comparative design (each participant with himself) for the larger study.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".