Bibliographic record
Abstract
The purpose of this research was to understand parental involvement in children’s one-on-one instrumental music learning and to examine how parents’ personal backgrounds influence their involvement. Parents with a child between 6 and 11 years old who takes classical one-on-one music lessons on a regular basis were recruited throughout Canada, and a sample of 71 participants completed an online questionnaire. The research sought to answer the following primary questions:a. How are parents involved in their children’s instrumental learning in terms of (a) attending lessons, (b) supporting their child’s home practice, (c) communicating with the teacher, (d) doing musical activities that are related to music learning and (e) hiring a teacher?b. Does the personal background of the parent make a difference in his or her involvement?The results suggest that parents’ different backgrounds, such as number of children, culture and level of musical training, make a difference in how they engage in attending lessons, supervising home practice, communicating with the teacher, employing a teacher and providing a musical environment to the child. For instance, the number of children affected parental attendance at lessons while their musical training influenced how they supervise home practice and provide a musical environment for the child. As this study suggests that the parents’ individual differences influence their involvement, instrumental teachers should be more aware of these factors to provide appropriate guidance on parental involvement according to the needs of each student and his or her parents. Regular communication about difficulties or expectations should play an essential role in the growth of the child’s music learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".