Effects of Non-Musical Attributes and Excerpt Duration on Ratings of High-Level Piano Performances
Bibliographic record
Abstract
The purpose of this study was to determine if judgments of expert piano performances would be affected by excerpt duration, non-musical attributes of performer attractiveness, dress, and stage behavior, and by an interaction between duration and these attributes. Given that non-musical attributes would be evident from the beginning of a performance but that information about performance quality would accrue with time spent listening, it was hypothesized that non-musical attribute biases would weaken with increasing excerpt length. Thirty-three undergraduate and graduate music majors rated performers on non-musical attributes by observing performers with the sound turned off. One hundred fourteen participants rated 15 performances on 6 test items, either under an audio-only condition or under an audiovisual condition. Excerpts were 25, 55, and 115 seconds. Results showing that high-attractive women held an advantage over low-attractive women for 25-second excerpts but not for longer ones confirmed the hypothesis that the importance of attractiveness declines with increased musical exposure. This hypothesis was less strongly supported for ratings of male performers, for whom differences in dress appeared to be of greater import. It was also found that there was significantly greater agreement between test items within excerpt durations than across them. Ratings were higher at 55 seconds and 115 seconds than they were at 25 seconds, although reliability declined slightly as excerpts became longer. Finally, performances judged under the audiovisual condition were rated significantly higher than performances judged under the audio-only condition.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".