Ensemble pitch and rhythm error discrimination : the identification and selection of predictors
Bibliographic record
Abstract
This study investigated relationships between 36 predictor variables and ensemble pitch and rhythm error discrimination ability. Precollege musical background and other demographic data were collected by means of the Musical Background Questionnaire. Musical achievement was measured by the Aliferis-Stecklein Music Achievement Test, College Midpoint Level. Undergraduate musical coursework data were obtained from transcripts. The criterion variables were measured by the Ramsey-Vincent Test of Instrumental Error Detection; a test of aural-visual pitch and rhythm error discrimination for full-score band music of medium difficulty. All three instruments were administered to 82 undergraduate music students. Subjects represented three Canadian universities and two community colleges. Pearson product-moment correlation tests were used to identify variables significantly related to musical ensemble error discrimination at the .10 level of significance. Eighteen variables were found to be significantly related to ensemble pitch error discrimination. Fourteen variables were found to be significantly related to ensemble rhythm error discrimination. Regression procedures were performed for each of the significant variables. These variables were then organized into blocks representing precollege musical background, other demographic variables, musical achievement, and undergraduate coursework. Regressions were performed for each of the blocks. Musical achievement, precollege musical background, demographic, and undergraduate coursework blocks of variables accounted for 5, 15, 35, and 21 percent of the variance in ensemble pitch error discrimination scores respectively. Musical achievement, precollege musical background, demographic, and undergraduate coursework blocks of variables accounted for 21, 16, 19, and 12 percent of the variance in ensemble rhythm error discrimination scores respectively. Combinations of variables from these blocks produced a linear model comprised of five demographic variables plus precollege choral experience that accounted for 42 percent of the variance in ensemble pitch error discrimination scores. Combinations of variables from the four blocks produced a linear model of ensemble rhythm error discrimination comprised of rhythmic discrimination, choice of a band instrument as one's major performance medium, composition as one's program major, and precollege band or orchestral experience. These four variables accounted for 32 percent of the variance in ensemble rhythm error discrimination scores. The variables selected for use in this study accounted for a substantial portion of the variance in error discrimination scores. To improve the predictive power of future studies, other variables need to be identified and included in the model. Ten conclusions were made regarding the prediction of ensemble error prediction ability. Three recommendations were made for improving error discrimination training and seven recommendations were made for future research in ensemble error discrimination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".