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Record W2940437308 · doi:10.14288/1.0378280

Serving two masters : Hummel's arrangements of Mozart's Piano Concerto in C major, K. 503 (ca. 1828)

2019· article· en· W2940437308 on OpenAlexaff
Irene Margarete Setiawan

Bibliographic record

VenuecIRcle (University of British Columbia) · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConcertoMOZARTPianoArtLiteraturePhysicsArt history

Abstract

fetched live from OpenAlex

Johann Nepomuk Hummel (1778-1837), once a pupil of Wolfgang Amadeus Mozart (1756-1791), made seven arrangements of Mozart’s Piano Concertos. Most studies of Hummel’s arrangements of Mozart’s Piano Concertos have focused on their reception history and Mozart’s performance practice. However, few have studied Hummel’s approach to piano arrangement, particularly his arrangement of Mozart’s Piano Concerto in C major, K. 503 (ca. 1828). To better understand Hummel’s approach to arrangement, I borrow concepts from translation theories. In this thesis, I adopt the concept “to serve two masters” from philosopher Franz Rosenzweig’s theory of translation. Similar to a translator who serves two masters (i.e., the original author and readers in the target language), an arranger who serves two masters pays equal attention to the composer of the original and the target audience of the arrangement. Using K. 503 as a case study, I investigate in this thesis how Hummel mediated between Mozart and the early nineteenth-century audience. I discover that Hummel added ornamentation to select themes and reinforced select closing passages. While he made the original more virtuosic in his arrangement, his ornamentation and modifications stay close to Mozart’s original thematic materials. As he used these techniques to satisfy the nineteenth-century audience’s need for piano virtuosity while adhering closely to Mozart’s intent, I argue that he served two masters. The implication of this thesis is that performers can take Hummel’s arrangement as a model that seeks not only to serve the composer but also to serve the target audience.

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)0.001
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 score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.009
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.175
Teacher spread0.162 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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