MétaCan
Menu
Back to cohort
Record W4406089903 · doi:10.1525/res.2024.5.4.439

A Letter in Times of Genocide

2024· article· en· W4406089903 on OpenAlexaboutno aff
Carolyn Ramzy

Bibliographic record

VenueResonance The Journal of Sound and Culture · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGenocidePolitical scienceCriminologySociologyLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.088

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.277
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueResonance The Journal of Sound and CultureSame topicGlobal Peace and Security DynamicsFrench-language works237,207