Engaging Indigenous Voices in the Academy: Indigenizing Music in Canadian Universities
Bibliographic record
Abstract
Canadian universities are under increasing pressure to address systemic exclusions of Indigenous peoples and knowledges, exclusions that have been perpetrated by residential schools, day schools, and universities. Canadian universities have, increasingly, begun to engage in Indigenization efforts; however, engagement in Indigenization efforts has been inconsistent and uneven within institutions and across the sector. Higher music education, in particular, has done little in terms of engaging in Indigenization, and this research seeks to address this gap. This research engages with six Indigenous musicians who each hold at least one university degree in music in order to hear their experiences in higher music education and to envision an Indigenized higher music education in Canadian universities. This research is, in part, a response to the Truth and Reconciliation Commission of Canada’s 94 Calls to Action (2015c), specifically Call 62, ii.: We call upon the federal, provincial, and territorial governments, in consultation and collaboration with Survivors, Aboriginal peoples, and educators, to: ii. Provide the necessary funding to post-secondary institutions to educate teachers on how to integrate Indigenous knowledge and teaching methods into classrooms (p. 331). Call to Action 62, ii provides a framework for thinking about and envisioning what higher music education—or post-secondary music programs—can do to respond to the TRC’s Calls to Action (2015c) in meaningful ways in collaboration with Indigenous peoples, communities, and Nations. This research engages with Ermine’s (2007) conceptualization of the ethical space and McCallum and Perry’s (2018) structures of indifference in order to conceptualize higher music education as structures of difference whereby Indigenous musics, musicians, knowledges, and musical practices are valued and included in ways that go well beyond Indigenous inclusion (Gaudry Lorenz, 2018). The final chapter includes a lengthy list of recommendations, informed by the participants’ stories, for all stakeholders in higher music education, including deans and senior leadership, chairs, faculty, staff, students, patrons and donors, and audience members. The academy has undergone very little structural change (Ottmann, 2013) and the magnitude of what will be required of higher music education in the work of Indigenization will necessitate visionary and courageous leadership. Keywords: music, higher music education, Indigenization, decolonization, reconciliation, post-secondary music education, university, Indigenization of the academy, Indigenization of higher music 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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.104 | 0.049 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".