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Record W2604518621 · doi:10.30535/mto.23.1.4

Humorous Script Oppositions in Classical Instrumental Music

2017· article· en· W2604518621 on OpenAlexaff
James K. Palmer

Bibliographic record

VenueMusic Theory Online · 2017
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInstrumental musicArtClassical musicLiteratureAestheticsMusical

Abstract

fetched live from OpenAlex

Most of us can recall chuckling, or even laughing out loud, at a humorous musical passage and perhaps recalling how much that experience increased our enjoyment of the music. This article focuses on musical humor in passages from instrumental works by Joseph Haydn, Michael Haydn, and Mozart. In the most general sense, musical humor arises when composers play with established conventions of musical discourse by writing something incongruous according to the stylistic context. I begin by briefly discussing the role of contrast in establishing musical humor in both historical and modern writings. I then introduce a strategy by which Classical composers created musical humor. I call this strategy “script opposition,” following linguistic theories of verbal humor. In my analytical discussion, I explain how “valence shifts” between implications of “high” and “low” create script oppositions, and demonstrate how these valence shifts are produced primarily by musical topics, but are bolstered by formal functions and cues in other musical parameters. My analytical and theoretical approach to musical humor draws on recent studies of musical topics, form, and communication in the Classical style, as well as concepts from recent linguistic theories of verbal humor.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.358
Teacher spread0.287 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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