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Record W1132614342 · doi:10.5206/notabene.v8i1.6598

Sonic Stereotypes: Jazz and Racial Signification in American Film and Television Soundtracks

2015· article· en· W1132614342 on OpenAlexaffvenue
Kyle Jackson

Bibliographic record

VenueNota bene · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsWestern University
Fundersnot available
KeywordsJazzNarrativeArtAestheticsSociologyWhite (mutation)Popular musicGender studiesLiteratureVisual arts

Abstract

fetched live from OpenAlex

This paper examines the use of jazz in contemporary American film and television soundtracks. Through processes of cultural signification, jazz music frequently maps racialized meaning onto the narrative. Often, a “black” jazz aesthetic signifies social and sexual deviance, while a “white” jazz aesthetic signifies elegance and high-culture. Such associations reinforce racial boundaries and essentialist stereotypes by perpetuating a dichotomy in which “blackness” figures as culturally dangerous (e.g. sexually deviant, unrestrained, threatening, and low-class) and “whiteness” as elite and culturally superior (e.g. civilized, educated, and high-class). To demonstrate this, the soundtracks of Anthony Minghella’s The Talented Mr. Ripley (1999); Ryan Murphy and Brad Falchuk’s American Horror Story: Coven (2013); and, Howard Gordon and Alex Gansa’s Homeland (2011) are examined. These examples are then compared to Spike Lee’s Do The Right Thing (1989), a film featuring sympathetic representations of African American identity. In Lee’s film, jazz challenges—rather than reinforces—racial discourse; a characteristic likely linked to Lee’s background as an African American with parents involved in the arts, black literature, and jazz composition. By comparing Lee’s alternative use of jazz to the preceding examples, it is argued that the use of the genre in film and television soundtracks as a stereotyping device reflects racial biases prominent in contemporary culture.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.405

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 studies0.0000.000
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 head0.038
GPT teacher head0.236
Teacher spread0.198 · 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
Published2015
Admission routes2
Has abstractyes

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