Effects of Selected Variables on Musicians’ Ratings of High-Level Piano Performances
Bibliographic record
Abstract
The purpose of this study was to ascertain whether judgments of solo performances recorded at a well-known international piano competition would be affected by musical characteristics such as style (classic period versus early 20th-century Russian) and tempo (slow versus fast). Evaluators rated performances on six test items: tone quality, note accuracy, rhythmic accuracy, expressiveness, adherence to style and overall impression. The effects of four between-subjects variables were examined: audio versus audiovisual presentation, undergraduate versus graduate/faculty, gender, and pianists versus non-pianists. Results from the 227 evaluators revealed main effects for treatment, style and major: audiovisual presentations were rated higher than audio only presentations, performances of Russian music were rated higher than performances of classic music, and pianists rated performances higher than did non-pianists. The treatment effect was due to a significant treatment by major interaction, and applied only to non-pianists. Gender was involved in a number of three- and four-way interactions that are difficult to interpret. Pearson r correlations were calculated for ratings of the 16 performances on the overall impression test item. The mean correlation for the 120 pairs of ratings was .33 – low but statistically significant. Intraclass correlations revealed no significant differences between the two levels of all four between-subjects variables.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".