Exploring motivational patterns in high-performing pianists: evidence from Cliburn competitors’ biographies
Bibliographic record
Abstract
This research examines the motivational patterns of high-performing classical pianists, characterized by a combination of implicit motives (i.e., non-conscious preferences for specific incentives). Utilizing the Linguistic Inquiry and Word Count (LIWC) software, I analyzed textual data from biographies of 107 pianists (i.e., Juniors aged 13–17: n = 38; Professionals aged 18–30: n = 30; Amateurs aged 35 and older: n = 39) participating in the prestigious 2022–2023 Van Cliburn Competitions. My results showed distinct profiles of implicit motives among pianists compared to non-pianists, with significantly higher need for achievement and need for power. While professional pianists exhibited the lowest level of need for power, junior pianists demonstrated the highest level of need for affiliation. Gender and age predicted part of pianists’ implicit motives. Male pianists demonstrated higher need for achievement than females. Finally, age negatively predicted need for affiliation. These findings highlight the motivational patterns within the classical piano community, offering theoretical implications for understanding implicit motives and practical applications for pianist education. Study limitations and future research directions are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".