Rethinking “musical excellence” from a decolonial perspective: Disruptive autobiographical experiences among doctoral scholars
Bibliographic record
Abstract
This collaborative autoethnography was developed by recent doctoral students in music from Southern Europe, Eurasia, East Asia, and North America, along with a professor based in Northern Europe. Our primary research question is “What can disruptive autobiographical experiences teach us about the implications of the decolonization movement for redefining “musical excellence” in higher music education?” The co-authors interviewed each other for their respective personal narratives on this theme, then collaboratively coded, analyzed and developed their results and interpretations. Four sub-questions served as prompts: (1) What was your gateway into music and how did the music learning-tradition that you were exposed to affect your development as a musician? (2) In what ways was the concept of “musical excellence” a part of your (early) development as a musician? (3) How does the concept of “musical excellence” impact how being an “artist” is defined by you and people around you? (4) How did this perception of “what is an artist” affect your musical path (and even how others perceive your career)? We share our findings and discuss implications in terms of possible innovations to higher music education, definitions of “musical excellence,” approaches to evaluation, and the role of competition in 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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.044 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".