Ensemble pitch and rhythm error discrimination : the identification and selection of predictors
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».