On Music Theory, and Making Music More Welcoming For Everyone by Philip Ewell (Book)
Bibliographic record
Abstract
Philip Ewell is perhaps the most (in)famous living music theorist.After beginning his career in the discipline as a scholar of Russian music and theories, Ewell shifted focus to the topic that has led to his notoriety: exposing the white supremacist 1 structure of music theory, with a focus on the central role of Heinrich Schenker in the development of contemporary music theory in the United States (and by extension, Canada).Ewell expands this focus in his new book, On Music Theory, and Making Music More Welcoming for Everyone, and also offers greater insight into the personal and professional experiences since 2019 that have shaped disciplinary discourse on these topics.It is an essential component of a longer anti-racism reading list for anyone engaging in academic music disciplines today.Given the topic of this book, it is important to state my positionality at the outset, particularly as it impacts my review.Among my many identities, I am a white, cisgender heterosexual woman, a non-Indigenous settler Canadian who used to be a professional music theorist and now works in educational development supporting teaching and learning initiatives related to equity, diversity, and inclusion.All of these identities impact my positionality when engaging with Ewell's book, but in particular I want to name my whiteness.This social identity means that I am part of the dominant race that is the main target of the book's critiques, and I benefit from the power and privilege this belonging affords me in music theory and society at large.My whiteness makes a critique of this book difficult.I regularly engaged with my own critical self-awareness to question whether my responses to particular passages were legitimate concerns or white fragility responses.2 Even if on careful self-examination I believe my critiques to be valid, they might still be taken as anti-Black racism given my white identity and the highly charged and polarized nature of these conversations both within music theory and in society at large.I hope this review is received in a spirit of generosity, collegiality, self-reflexivity, and a desire to continue to dialogue across difference in the grey areas between polarized positions, but I acknowledge that its impact might not be thus.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".