On Embedding Indigenous Musics in Schools: Examining the Applicability of Possible Models to One School District’s Approach
Bibliographic record
Abstract
Recent curriculum policy changes in British Columbia (BC) require that educators in all subject areas-including music-embed local Indigenous knowledge, pedagogies, and worldviews in their classes. Yet facilitating such decolonizing cross-cultural music education activities requires knowledge that music educators may not currently possess. We use four models created by an Indigenous Arts scholar to examine the interface of Indigenous and Western art musics in performing arts settings: (a) integration, (b) nation-to-nation music trading and reciprocal presentation, (c) a combination of the first two models, and (d) non-integrative encounters that are in relationship but have irreconcilable elements. We consider the applicability of these models in music education settings, using them to analyze our findings from a study in which we explored the ways teachers have embedded local First Nations songs and drumming in classes in a single metropolitan school district in BC.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".