Integrating South Asian Music into Alberta's Music Curriculum: Guiding Music Educators on How to Improve and Enact Upon Existing Teaching Practices
Bibliographic record
Abstract
Growing up as a South Asian–Sri Lankan student, I recall having a deep desire for cultural representation in school. Even in my own teaching career I struggled to find South Asian music resources, and that eventually compelled me towards research in this field. This study employed an Action Research methodology situated within an anti-colonial framework which allowed me the opportunity to highlight the participants’ positionalities, critically question what music best serves the students of the classroom, resist colonial practices, and institute social change by creating a new way forward. The integration of South Asian music was facilitated using World Music Pedagogy as a pedagogical framework. This framework enabled the inclusion of South Asian and Western pedagogical practices that generated opportunities to compare similarities/differences, build, and recognize interconnections of the socio-cultural-musical contexts of the musics learned. The goal of this research was to create a curricular balance of windows, mirrors, and sliding glass doors in order to accurately reveal and reflect the students’ realities; consequently, enabling for the composition of a culturally responsive classroom in which both teachers and students felt empowered. This study considered the effectiveness of this implementation and how it assisted in the development of the students’ self-identities.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".