To Honor and Inform: Addressing Cultural Humility in Intercultural Music Teacher Education in Canada
Bibliographic record
Abstract
Abstract In this chapter I address the need for reshaping the way we think about Indigenous inclusion in the intercultural curriculum. While Canada has prided itself on its multicultural heritage, the nation’s relationship with the First Peoples – First Nations, Inuit and Métis – has been immoral, genocidal and assimilationist. The 2015 publication of the findings of the Truth and Reconciliation Commission’s Calls to Action details a way forward from the colonial patriarchy of the past. Education in general, and music education in particular are charged with finding ways to incorporate Indigenous knowledge, perspectives and history into the curriculum. After an introduction to the issues of the marginalization of Indigenous voices I discuss several arts-based curricular and extra-curricular initiatives that reframe intercultural music education. I propose that developing cultural humility in music teacher education will be a step forward in the decolonization of our teaching and learning spaces. This includes a move from generic cultural competencies toward an attitude of ‘Cultural Humility’. Cultural Humility is discussed as an attitude toward engagement with peoples of cultures other than one’s own. The stance is based on life-long, self-reflective inquiry, and seeks to disrupt the power imbalance that defines ‘othering’, seeking to establish partnerships and collaboration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".