Comparison Research on Musical Literacy Education in Curriculum of Pre-service Teachers between China and Canada from Perspectives of Reciprocal Learning: A case study
Bibliographic record
Abstract
Cross-cultural reciprocal learning has been adopted as a new perspective in international education comparison study. And Education internationalization has enhanced the openness, fusion, interaction and sharing of learning style. The case study compared the differences of curriculum purpose, principles, context, methods and practice in music literacy education between Canada and China for pre-service teachers who do not work as music teachers in future . Most primary and secondary school teachers in Canada are general teachers who needs to teach subjects other than music,. While, Primary and secondary School subject teachers in China also need to have general knowledge in teachig practice. Both countries treat music literacy as one of core professional qualities in teacher education and evaluation. Therefore, it is meaningful for both countries to conduct researches on curriculum structures and teaching approaches of pre-service teacher education of each others. China and Canada have individual features in musical courses for pre-service teachers but still share something in common. Through a longitude comparison study, the research intends to realize the purposes of reciprocal learning in teacher education, and contribute to further research in cross-cultural teacher education.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| 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".