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Record W2909964415 · doi:10.1177/2059204318802505

“Equiheptatonic” Tuning in Thai Classical Music: Strict Propriety and Step Sizes

2019· article· en· W2909964415 on OpenAlexaff
Jay Rahn

Bibliographic record

VenueMusic & Science · 2019
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsYork University
Fundersnot available
KeywordsSpan (engineering)Interval (graph theory)Scale (ratio)Verifiable secret sharingMathematicsLife spanFalsifiabilityComputer scienceStatisticsAlgorithmCombinatoricsPhysicsSet (abstract data type)Structural engineeringEngineeringGeographyBiologyEvolutionary biologyCartography

Abstract

fetched live from OpenAlex

Tunings of Thai classical music have been a source of disagreement during the past century. Focusing on 28 ensembles, the present study analyzes ways in which the intervals they produce can be formulated so that they are both falsifiable and verifiable. Of these, a model that corresponds to Rothenberg’s formulation of strict propriety excels among pairs of tones that span different numbers of scale degrees. According to Rothenberg’s model of strict propriety, intervals that span fewer scale degrees are smaller than intervals that span more scale degrees. Further, according to a formulation of clear patterning among intervals that span precisely two scale degrees (i.e., a single step), there is no clear pattern of one-step intervals unless all the instances of at least one interval that spans two particular consecutive scale degrees are smaller and/or larger than all the instances of all the other intervals that span two consecutive scale degrees. Among the 28 ensembles, single-step intervals tend to constitute chains that overlap in size rather than a clear pattern of small and large intervals.

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)0.007
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.002
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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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