Bibliographic record
Abstract
At the Society for Ethnomusicology’s 68th annual conference in Ottawa, Canada, several Indigenous graduate students, postdocs, as well as un/tenured faculty teamed up to present the conference keynote known as the Charles Seeger Lecture. Titled “Listen, Watch Your Step,” the performative lecture aimed to unsettle the “structural normativity that conscripts Indigenous experience to a narrow range of telling, sensing, and feeling” in the field of music knowledge-making and research. Importantly, the collaborators invited listeners to think carefully about the “structural weaknesses, epistemological myopia, and material inequalities” that often shape Indigenous experiences in conference settings, and by extension, in the field of ethnomusicology. When Indigenous presenters highlighted their recognition of another settler-colonial cleansing—this time in the ongoing genocide in Gaza—they were met with disruption and hostility from the audience, who were effectively refusing to listen to the ways ethnomusicology continues to be implicated in settler-colonial politics. In this letter addressed to the Indigenous participants of the Charles Seeger Lecture, I reflect on the dissonances of a discipline of the ear but one that does not yet listen. And I contemplate the power of divestment following the continued silencing of the society’s Indigenous, Black, and racialized members when raising issues of racism in our discipline: What would ethnomusicology be without our voices, bodies, stories, and without our bones?
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".