A Geographical Analysis of Canadian Students Taking Independent Music Lessons: The Rural Experience
Bibliographic record
Abstract
The engagement of students taking private music lessons is affected by a range of factors, one of which is the geographic location of the student’s family. This is a geographical analysis of 6,500 questionnaire responses completed by Canadian music teachers, students, and parents, including 819 responses (12.6%) from participants living in ‘rural’ areas, as defined by Statistics Canada. Participants’ home locations were categorized on a five-point ordinal scale from ‘rural’ to ‘very large urban population center’, data-matched with further geospatial data relating to deprivation and road distances, and assessed for strength and direction of association with questionnaire items. Results revealed that students living in more rural areas performed more regularly than those in more urban areas, with parents and teachers in more rural areas taking greater part in collective music making events. Whilst they derived a smaller proportion of their household income from music, teachers in more rural areas garnered greater respect from parents. Parents also reported increasing pleasure in children’s musical progress as population centers decreased in size. The results offer tentative support to the view that in more rural situations, where there are potentially fewer students and teachers, closer intergenerational bonds are possible, and more aspects of private music lessons might reflect locally-valued traditions and resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".