Teaching and Learning Popular Music in Higher Education Through Interdisciplinary Collaboration: Practice What You Preach
Bibliographic record
Abstract
This article provides a contextualized explanation of an emerging strategy for popular music teaching and learning in higher education that the authors term Improvisatory Integrative Learning. This strategy coalesces around four themes from a Do-It-Yourself and Do-It-With-Others ethos: autonomy, play, peer learning, and peer teaching. To explicate the possibilities and pitfalls of teaching popular music in this way, the authors analyze the approaches taken in a co-taught university course integrating two perspectives: music education and ethnomusicology. The interdisciplinary collaboration became an investigative space for informal music learning approaches in a formal context, in which students improvised with creative composition. We explore not only how processes that are part and parcel of popular music learning can help improve productivity in a popular music classroom, but also the ways that improvisatory integrative learning can serve a diverse university student population by expanding interdisciplinary approaches to multiple kinds of subject matter.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".