Music motivation and the effect of writing music: A comparison of pianists and guitarists
Bibliographic record
Abstract
The purpose of this study is to examine a set of motivation variables within guitar and piano players. We also tested for motivational differences among three groups: those who write music, those who plan to write music in the future, and those who do not write nor intend to write. An international sample of 599 musicians was obtained (guitar: N = 292, piano: N = 307) through the use of an online survey. Self-Determination Theory, a prominent perspective in the motivation literature, was utilized along with other motivational constructs, including perceived competence, musical self-esteem, effort, desire to learn, willingness to play, and possible musical selves. Findings revealed differences between pianists’ and guitarists’ levels of motivational intensity, desire to learn, introjected regulation, perceived competence and willingness to play. Results also indicated that the group who write music had significantly higher levels of musical self-esteem, willingness to play, motivational intensity, desire to learn, and perceived competence. Findings from this study suggest that pianists and guitarists both are intrinsically motivated, but for different reasons. The underlying motivational needs that are met by the instrument’s “culture” appear to focus on competence for pianists and on autonomy and relatedness for guitarists.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".