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Record W2573249804 · doi:10.1111/musa.12084

Two Approaches to Tonal Space in the Music of Muddy Waters

2017· article· en· W2573249804 on OpenAlexaboutno aff
Ben Curry

Bibliographic record

VenueMusic Analysis · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSonority hierarchyChord (peer-to-peer)BluesMelodySpace (punctuation)TimbreSpeech recognitionComputer scienceRepertoireCategorizationMajor and minorTonalityLinguisticsMathematicsMusicalAcousticsArtificial intelligenceArtVisual artsPhilosophyPhysics

Abstract

fetched live from OpenAlex

ABSTRACT This article posits two approaches to blues tonal space: the networked approach and the laddered approach. The first is underpinned by the cyclical potential of equal temperament and is characterised by descending chromatic motions. The second is underpinned by the harmonic series and is characterised by microtonal shaping of pitch content within the framework of a ladder that approximates an equal‐tempered major‐minor seventh chord. Having explained the detail of these approaches to tonal space with reference to an early blues recording of Alberta Hunter and Fats Waller, the article goes on to examine Muddy Waters's use of the laddered approach to tonal space, but also develops novel ways of referencing the networked approach and of finding synergies between networked and laddered approaches. Analysis of key songs from Waters's repertoire offers insight into the interchange between networked and laddered tonal space in blues music. This interchange holds the major‐minor seventh chord to be structurally significant owing to the weight afforded by the sonority of the minor third and the affinity between microtonal and semitonal melodic motions.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.012
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.244
GPT teacher head0.263
Teacher spread0.018 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes1
Has abstractyes

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