Decolonizing and Indigenizing Music Education through Self-Reflexive Sociological Research and Practice
Bibliographic record
Abstract
Abstract In this chapter, Canadian authors Anita Prest and Scott Goble use sociological lenses to illustrate how, as non-Indigenous researchers, they learned to immerse themselves in local Indigenous knowledge(s) in western Canada. Such learning enabled them to reframe their investigations to reflect the ontological and epistemological orientations of the communities with whom they work. They submit that such immersion and questioning is necessary for decolonizing practices in music education and research so as to fairly represent the worldviews of local First Nations. Drawing on the problematics with new materialism as well as their personal struggles for self-reflexivity, they demonstrate the challenges involved in decolonizing personal epistemological perspectives. Their chapter provides an example of the work to be done in reframing a sociologically informed 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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.099 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".