Bibliographic record
Abstract
Abstract This chapter argues that while at first glance music education policy and music composition may seem an odd pairing, when framed through a lens of critical and progressive practice the strangeness disappears. The chapter illustrates a pushback at traditionalist, Eurocentric narratives, arguing that in some ways, apprehensions around “policy” mirror hesitancies around “composing”—a similar narrowness promoting misconceptions about who can do what and how and to what end. What can be heard, instead, is an invitation for music teachers in all educational levels to reimagine their roles and (re)sound music education as a space for voice, agency, and social change. Situating itself within larger—and now acute—social, racial, and cultural demands placed by a need to change, this chapter explores avenues through which music education may be ready to enact curricular policies that forego “conveyor belt” approaches (Stone, 2011) to recurring challenges such as music teacher preparation diversity, pedagogical innovation, and program relevance. The chapter presents three field cases that exemplify innovative dispositions and provides a critique of music teacher education programs in relation to NASM accreditation policy. Specifically, we use policy as an analytical lens to articulate how composition, understood broadly, can serve as a convincing example of “institutional innovation capable of linking the subject in a creative relationship with an [renewed] institutional environment.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.072 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".