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Record W1512589837 · doi:10.30535/mto.19.1.5

Beyond Homage and Critique? Schubert’s Sonata in C minor, D. 958, and Beethoven’s Thirty-Two Variations in C minor, WoO 80

2013· article· en· W1512589837 on OpenAlexaff
René Rusch

Bibliographic record

VenueMusic Theory Online · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsAppropriationMinor (academic)OriginalityLiteratureMusicalArtMusical analysisPhilosophyPiano sonataMOZARTHumanitiesArt historyPsychologyLinguistics

Abstract

fetched live from OpenAlex

When Schubert’s instrumental pieces seem to directly quote or allude to Beethoven’s works, some music scholars have interpreted these forms of appropriation either as musical homage or as evidence that Schubert modeled several of his works on Beethoven’s. Other scholars have encouraged us to rethink these perspectives, suggesting instead that the same forms of appropriation can be read as active responses or antipodes to Beethoven’s music. This paper reconsiders the topic of influence in Schubert’s music from a post-structuralist position, drawing from Jacques Derrida’s writings on grafting—the act of placing separate texts side by side to produce a new structure. Using the first movement from Schubert’s Sonata in C minor, D. 958, and Beethoven’s Thirty-Two Variations in C minor, WoO 80 as examples, my paper seeks to rethink the categories of homage and critique by considering the following two ideas: (1) if “[t]o write means to graft” (Derrida [1972] 1982, 355), each composition contains a heterogeneity of texts, challenging the possibility of an original text; (2) matters concerning appropriation do not lie solely within either musical text, but rather between them, inviting us to reconsider how constructions of history and criteria for originality can affect our understanding of appropriation and our music-analytical readings of Schubert’s works.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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