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Sound Relations

2021· book· en· W3212923865 on OpenAlexaboutno aff
Jessica Bissett Perea

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSound (geography)HistoryAcousticsPhysics

Abstract

fetched live from OpenAlex

Abstract Sound Relations: Native Ways of Doing Music History in Alaska delves into histories of Inuit musical life in Alaska to amplify the broader significance of sound as integral to Indigenous self-determination and resurgence movements. The book offers relational and radical ways of listening to a vast archive of Inuit presence across a range of genres—from hip hop to Christian hymnody and drumsongs to funk and R&B—to register how a density (not difference) of Indigenous ways of musicking invites readers to listen more critically to and for intersections of music, Indigeneity, and colonialism in the Americas. The research aims to dismantle stereotypical understandings of “Eskimos,” “Indians,” and “Natives” by considering how Indigenous-led and Indigeneity-centered analyses of Native musicking can reframe larger debates of race, Indigeneity, power, and representation in twenty-first-century American music historiography. Instead of proposing singular truths or facts, this book asks readers to consider the existence of multiple simultaneous truths, a density of truths, all of which are culturally constructed, performed, and in some cases politicized and policed. A sound relations approach advances a more Indigenized sound studies and a more sounded Indigenous studies that works to move beyond colonial questions of containment—“who counts as Native” and “who decides”—and colonial questions of measurement—“what exactly is ‘Native’ about Native music”—and toward an aesthetics of self-determination and resurgent world-making.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.464
Threshold uncertainty score0.765

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0120.008
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.4640.213

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 head0.042
GPT teacher head0.189
Teacher spread0.148 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations53
Published2021
Admission routes1
Has abstractyes

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