Serving two masters : Hummel's arrangements of Mozart's Piano Concerto in C major, K. 503 (ca. 1828)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".