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Record W4214490721 · doi:10.5749/9781452965963

Sounds from the Other Side

2020· book· en· W4214490721 on OpenAlexfundno aff
Elliott H. Powell

Bibliographic record

VenueUniversity of Minnesota Press eBooks · 2020
Typebook
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsnot available
FundersYork UniversityTemple UniversityUniversity of MinnesotaHarvard UniversityPrinceton University
KeywordsComputer science

Abstract

fetched live from OpenAlex

From Beyoncé’s South Asian music-inspired Super Bowl Halftime performance, to jazz artists like John and Alice Coltrane’s use of Indian song structures and spirituality in their work, to Jay-Z and Missy Elliott’s high-profile collaborations with diasporic South Asian artists such as the Panjabi MC and MIA, African American musicians have frequently engaged South Asian cultural productions in the development of Black music culture. *Sounds from the Other Side* traces such engagements through an interdisciplinary analysis of the political implications of African American musicians’ South Asian influence since the 1960s. <br/><br/> Elliott H. Powell asks, what happens when we consider Black musicians’ South Asian sonic explorations as distinct from those of their white counterparts? He looks to Black musical genres of jazz, funk, and hip hop and examines the work of Miles Davis, John Coltrane, Rick James, OutKast, Timbaland, Beyoncé, and others, showing how Afro-South Asian music in the United States is a dynamic, complex, and contradictory cultural site where comparative racialization, transformative gender and queer politics, and coalition politics intertwine. Powell situates this cultural history within larger global and domestic sociohistorical junctures that link African American and South Asian diasporic communities in the United States. <br/><br/> The long historical arc of Afro-South Asian music in *Sounds from the Other Side* interprets such music-making activities as highly political endeavors, offering an essential conversation about cross-cultural musical exchanges between racially marginalized musicians.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.433
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.233
Teacher spread0.196 · 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
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

Citations0
Published2020
Admission routes1
Has abstractyes

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