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Record W2979520337 · doi:10.1017/s026114301900028x

Good things come in threes: triplet flow in recent hip-hop music

2019· article· en· W2979520337 on OpenAlexaff
Ben Duinker

Bibliographic record

VenuePopular Music · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsPopularitySalientBlueprintRhetorical questionStyle (visual arts)AestheticsSociologyLiteratureArtPsychologyVisual artsPolitical scienceLawSocial psychology

Abstract

fetched live from OpenAlex

Abstract MCs (rappers) such as Cardi B, Kendrick Lamar, Drake, Big Sean and Young Thug use triplet rhythms in their rapping, a practice that is known as triplet flow. This paper argues that the prevalence of triplet flow is one of the most aurally salient features of contemporary hip hop, and exemplifies the popularity and influence of the Atlanta-centred genre of trap music through its sparse, slow beats. Three types of triplet flow are defined – mixed, phrasal and total – and are used to explore how various songs and artists active in the late 1980s and early 1990s provided the stylistic blueprint for triplet flow's recent explosion in popularity. With the aid of a 50 song mini-corpus, the paper concludes with a general survey of stylistic characteristics common in many songs featuring triplet flow, and further analysis of two of these songs in order to illuminate the creative, rhetorical and virtuosic potential that underpins this ostensibly simple style of rapping.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.211
Teacher spread0.170 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations28
Published2019
Admission routes1
Has abstractyes

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