Introduction: Beyond Western Musicalities
Bibliographic record
Abstract
It has become increasingly clear that the way we teach music theory is not only incomplete, it is insufficient, even irresponsible. This introduction elucidates some of the key issues around diversifying and decolonizing music theory classrooms. Following a brief framing of the issues, a triptych of short thought pieces examines the contexts and implications of such questions. Dylan Robinson locates core curricula as a "ground" upon which music programs are built and which might be "given back" to BIPOC scholars to re-define; Anna Yu Wang advocates for classroom methodologies which center diverse modes of listening as foundations for music theorizing; and Maya Cunningham considers how Western music theory systems and training are historically colonial and continue to be driven by cultural and economic bias and inequity. Finally, in an ambitious co-authored work, Chris Stover, Leslie Tilley, and Anna Yu Wang organize, synthesize, and extrapolate from survey responses by twenty-four music scholar-pedagogues to offer a panoramic view on diversifying and decolonizing efforts. The essay addresses challenges, disagreements, goals, and possible ways forward, and through its explorations encourages sustained reflection, accountability, and change.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".