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Record W2942882990 · doi:10.30535/mto.25.1.6

Comprehensibility and Ben Johnston’s String Quartet No. 9

2019· article· en· W2942882990 on OpenAlexaff
Laurence Sinclair Willis

Bibliographic record

VenueMusic Theory Online · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntonation (linguistics)String (physics)MusicalVariety (cybernetics)Set (abstract data type)LinguisticsComputer scienceMusical formNegotiationCognitive scienceCommunicationArtPsychologyVisual artsSociologyArtificial intelligencePhilosophyPhysicsTheoretical physics

Abstract

fetched live from OpenAlex

Ben Johnston’s just-intonation music is of startling aural variety and presents novel solutions to age-old tuning problems. In this paper, I describe the way that Johnston reoriented his compositional practice in the 1980s as evidenced in his musical procedures. Johnston became aware of the disconnect between Western art music composers and their audiences. He therefore set about composing more accessible music that listeners could easily comprehend. His String Quartet No. 9 gives an instructive example of the negotiation between just intonation and comprehensibility. By integrating unusual triadic sonorities with background tonal relationships, Johnston reveals an evolution of just-intonation pitch structures across the work. This paper provides an example of an analytical method for exploring Johnston’s works in a way that moves beyond simply describing the structure of his system and into more musically tangible questions of form and process.

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)0.002
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.009
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.024
GPT teacher head0.216
Teacher spread0.192 · 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
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

Citations2
Published2019
Admission routes1
Has abstractyes

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