Listening with ‘Big Ears’: Accountability in cross-cultural music education research with Indigenous partners
Bibliographic record
Abstract
In this theoretical article, I examine various conceptions of focused listening-including those held by specific First Nations communities-to determine how each conception might offer insights for listening while conducting cross-cultural music education research. First, I discuss the notion of "Big Ears," as it is understood by the jazz community. Then, I turn to scholars from various First Nations in British Columbia to learn about their conceptions of listening. I outline decolonial listening strategies as proposed by Indigenous Arts scholar Dylan Robinson, before learning about the role of listening from a settler-Canadian who formally Witnessed the testimonies of Indigenous residential school survivors over a period of years while working for the Truth and Reconciliation Commission of Canada. I examine the writings of music education researchers who have proposed listening as an important strategy in cross-cultural/intercultural pedagogy and research, albeit in different circumstances and for different reasons. Finally, I describe/reflect on my process of learning to listen cross-culturally as a settler-Canadian music education researcher engaged in community-based participatory research (CBPR) over the course of three studies, and list some of the ongoing questions I have. I conclude by proposing a revised understanding of Listening with "Big Ears" as one possible way for non-Indigenous researchers using a CBPR approach to enhance their application of Indigenist research methodology, especially in demonstrating their accountability to Indigenous co-researchers, participants, and communities, as they engage collaboratively in music education research.
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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 | 0.166 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.041 | 0.088 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.006 | 0.010 |
| 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".