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Record W3037167409 · doi:10.1525/mp.2020.37.5.373

Classical Rondos and Sonatas as Stylistic Categories

2020· article· en· W3037167409 on OpenAlexaff
Jonathan De Souza, Adam Roy, Andrew Goldman

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyCategorizationHappinessPiano sonataMovement (music)Speech recognitionLinguisticsCognitive psychologyArtSocial psychologyComputer scienceAestheticsPhilosophy

Abstract

fetched live from OpenAlex

Sonata and rondo movements are often defined in terms of large-scale form, yet in the classical era, rondos were also identified according to their lively, cheerful character. We hypothesized that sonatas and rondos could be categorized based on stylistic features, and that rondos would involve more acoustic cues for happiness (e.g., higher average pitch height and higher average attack rate). In a corpus analysis, we examined paired movement openings from 180 instrumental works, composed between 1770 and 1799. Rondos had significantly higher pitch height and attack rate, as predicted, and there were also significant differences related to dynamics, meter, and cadences. We then conducted an experiment involving participants with at least 5 years of formal music training or less than 6 months of formal music training. Participants listened to 120 15-second audio clips, taken from the beginnings of movements in our corpus. After a training phase, they attempted to categorize the excerpts (2AFC task). D-prime scores were significantly higher than chance levels for both groups, and in post-experiment questionnaires, participants without music training reported that rondos sounded happier than sonatas. Overall, these results suggest that classical formal types have distinct stylistic and affective conventions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.072
GPT teacher head0.341
Teacher spread0.269 · 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.

Study designBench or experimental
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

Citations3
Published2020
Admission routes1
Has abstractyes

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