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Record W4386588512 · doi:10.18357/tar141202321373

Rap and Realism

2023· article· en· W4386588512 on OpenAlexaffvenue
Nadia Ekkel

Bibliographic record

VenueThe Arbutus Review · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAestheticsIdentity (music)RealismReputationSociologyDisadvantagedLegitimacyScholarshipArtPersonaPaintingMusicalVisual artsSocial sciencePolitical scienceLawPoliticsHumanities

Abstract

fetched live from OpenAlex

Over a century and a half since his passing, scholars remember Gustave Courbet (1819–1877) as a French painter who expanded the boundaries of art through his rejection of traditionally imposed artistic conventions and cultivation of a larger-than-life persona. As a master of self-promotion and image creation, Courbet’s unique self-positioning and performance of identity are frequently explored within contemporary scholarship. However, extant literature has yet to consider the similarities between Courbet’s performance of identity and that of influential contemporary rap artists. Looking to artistic themes employed by the painter and his self-presentation, I argue that Courbet tailored his identity performance so that audiences conflated his artistic output with his public image, both of which drew upon his rural background to suggest a disadvantaged socioeconomic status—a background lauded by his supporters as providing Courbet with the authenticity required for artistic legitimacy. Furthermore, I argue that contemporary rap artists perform their identity in much of the same manner. Like Courbet, contemporary rap artists continuously enact qualities associated with a low socioeconomic status and reference this social standing in their music to fuse their personal reputation and musical output, which affords these artists the authenticity required for success.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.551
Threshold uncertainty score1.000

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)0.0030.001

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 head0.065
GPT teacher head0.263
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes2
Has abstractyes

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