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Record W2777031040 · doi:10.1017/s1355771817000310

Sounding Riddims: King Tubby’s dub in the context of soundscape composition

2017· article· en· W2777031040 on OpenAlexaffabout
Nimalan Yoganathan, Owen Chapman

Bibliographic record

VenueOrganised Sound · 2017
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsSoundscapeComposition (language)Electroacoustic musicContext (archaeology)Visual artsSound designComputer scienceArtSociologySound (geography)HistoryLiteratureAcoustics

Abstract

fetched live from OpenAlex

A significant body of academic literature and music journalism has explored the historical trajectory of Jamaican dub music and its innovative use of audio recording technology. The present article seeks to demonstrate the similarities between the studio compositional methods of Jamaican dub innovator King Tubby and those of Canadian soundscape composers Barry Truax and Hildegard Westerkamp. Rather than attempting to identify aesthetic and stylistic similarities between Tubby’s dub music and soundscape composition, this article presents a comparative analysis of dub in relation to soundscape composition focusing on artistic articulations of contextual meaning and acoustic communication. Specifically, this work argues that Tubby’s compositional approach directly addresses the following conceptual themes common in soundscape composition: 1) referential composition and the invocation of past listening associations through sonic abstraction, 2) timbral play as a means of linking sound processing to acoustic communication, and 3) the evocation of real-world motion cues by way of ecologically informed sound-processing effects. Exploring the conceptual similarities between Tubby’s work and the established academic-affiliated genre of soundscape composition provides a new perspective on his work as reflecting a multifaceted musical approach that warrants further scholarly study.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.000
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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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 designTheoretical or conceptual
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

Citations1
Published2017
Admission routes2
Has abstractyes

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