Patriotism, nationalism, and national identity in music education: ‘O Canada,’ how well do we know thee?
Bibliographic record
Abstract
The purpose of the study was to determine Canadian secondary school choral students’ skill in singing the national anthem. The sample ( N = 275) consisted of students from 12 schools, representing six provinces in Canada. Students were audio taped singing ‘O Canada’ in English, French, or in a combination of both languages and subsequently completed a questionnaire. Results indicated that few students could sing the national anthem perfectly. Although students were significantly more accurate in remembering the lyrics than in singing the melody ( p < .0001), only 67% were judged proficient in lyrics whereas a mere 46% were judged proficient in melody. Possible reasons for these poor results include the frequency with which students sing the anthem in secondary schools, the fact that three-quarters named a classroom teacher in the early/elementary years as being the one responsible for teaching them the anthem, the shift to solo versus group singing in public events, and the inconsistency with which music education is delivered in elementary schools. Implications for practice indicate that more emphasis be placed on assisting choir members to sing the anthem accurately, more opportunities be provided in secondary schools for students to sing the anthem, and more curricular attention be placed on teaching students both English and French versions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".