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Record W4283123938 · doi:10.5206/notabene.v15i1.15032

“Be Yourself”: Frank Ocean’s Blonde and Hip-Hop

2022· article· en· W4283123938 on OpenAlexaffvenue
Benjamin Heffernan

Bibliographic record

VenueNota bene · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsWestern University
Fundersnot available
KeywordsMusicalMusical formStyle (visual arts)Identity (music)ArtArt historyAestheticsVisual artsLiterature

Abstract

fetched live from OpenAlex

Upon the release of Frank Ocean’s 2016 album, Blonde, its minimalist yet textured musical aesthetic saw critics compare the album to works by rock auteurs such as Brian Wilson and Brian Eno. However, Blonde, while not necessarily a hip-hop album, makes extensive reference to hip-hop in both its musical and lyrical content as well as its extra-musical implications. This essay argues that Blonde’s various connections to a “hip-hop tradition” are more important to understanding the album’s identity than any comparison to a rock-influenced musical aesthetic. This essay defines a “hip-hop tradition” as determined by factors other than the musical aesthetic of the released album, which include the album’s cultural standing, the intention behind the creation, composition or release of the work, the style of lyricism, and the artist’s history and identity: in Blonde’s case, all these factors remain largely indebted to hip-hop conventions. This type of consideration represents a more holistic method of assessing the core identity of a musical work as opposed to solely relying on sonic or commercial considerations. This paper next examines Blonde’s various connections to a “hip-hop tradition” through a study of Ocean’s own personal and musical history, Ocean’s rebellion against the neocolonial establishment of the music industry, his highly collaborative creative process, and the album’s hip-hop-influenced lyrical content.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.948

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0530.000

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 head0.030
GPT teacher head0.196
Teacher spread0.166 · 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.

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
Published2022
Admission routes2
Has abstractyes

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