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Record W2497896021 · doi:10.1017/ccol9780521833479.002

Haydn's career and the idea of the multiple audience

2005· book-chapter· en· W2497896021 on OpenAlexaff
Elaine R. Sisman

Bibliographic record

VenueCambridge University Press eBooks · 2005
Typebook-chapter
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMOZARTTone (literature)PersuasionMemoirMusicalPeriod (music)Balance (ability)ArtLiteraturePsychologyAestheticsVisual artsSocial psychology

Abstract

fetched live from OpenAlex

For whom did Haydn write? This simple question, easily enough answered by such obvious recipients as his patrons or the public or particular performers, masks a series of more complex questions about Haydn's career as well as about his muse. How did he balance his own desires with those of his patrons and public? How did he respond to the abilities of the performers, whether soloists, orchestral musicians, or students, for whom he composed? How did he seek to communicate with different audiences, and were his communicative strategies and modes of persuasion always successful? While these questions might be asked of any composer, especially those in the later eighteenth century who had to adapt to an evolving menu of career opportunities, they have special pertinence for Haydn, whose career and works reveal, as well as revel in, the idea of the multiple audience that emerged in this period. This essay will explore the ways in which the shape of Haydn's career, his sometimes inexplicably defensive tone in letters and memoirs, and his musical self-assessments stem from this new source of inspiration. It is perhaps not a coincidence that Haydn, unlike C. P. E. Bach, Mozart, and Beethoven, left no record of disparaging remarks about the public.

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)0.002
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.013
Scholarly communication0.0070.007
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.168
Teacher spread0.137 · 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

Citations49
Published2005
Admission routes1
Has abstractyes

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