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Record W4200150072 · doi:10.18061/fdmc.2021.0016

Section 2: Corpus Studies Abstracts

2021· article· en· W4200150072 on OpenAlexaff
C.A. Anderson, Michael Schutz, Hannah M. Merseal, Roger E. Beaty, Klaus Frieler, Martin Norgaard, Maryellen C. MacDonald, Daniel J. Weiss, Chi-Sing Siu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSection (typography)Computer scienceNatural language processingArtificial intelligenceInformation retrievalOperating system

Abstract

fetched live from OpenAlex

Diverging patterns in mode's associations with other musical cues carry distinctive expressive connotations.The major and minor modes' relationship with loudness and timing are often understood in absolute terms, with major pieces described as being faster and louder than their minor counterparts.However, recent findings suggest mode's relationship to other cues shifted markedly in the Romantic era (Horn & Huron, 2015).Here we expand on previous work using cluster analysis to track expressive changes in music history, applying this technique to Bach's The Well-Tempered Clavier (1722) and Chopin's Preludes (1839).Analyzing clusters of each composer reveals empirical support for mode's changing expressive associations.Specifically, Chopin's minor pieces are distinguished by fast attack rates and louder dynamics than Bach's, consistent with research highlighting mode's changing musical meaning.In tandem with our team's work performing perceptual experiments with these pieces, this analysis provides a valuable complement to the small but growing body of research exploring changes in the use of emotive acoustic cues over musical history.

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)0.007
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.830

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0190.025
Science and technology studies0.0040.002
Scholarly communication0.0060.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4180.146

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 head0.052
GPT teacher head0.378
Teacher spread0.326 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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