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Record W6989516643

Benjamin Britten: Composer as Conductor and the Art of Self Interpretation

2014· other· en· W6989516643 on OpenAlexaboutno aff

Bibliographic record

VenueArizona State University Library Digital Repository (Arizona State University) · 2014
Typeother
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPerforming artsInterpretation (philosophy)GermanRepertoireInterpreterKey (lock)
DOInot available

Abstract

fetched live from OpenAlex

abstract: In the triumvirate of composer-performer-listener, while the listener always wins, the performer is the interpreter through which the listener experiences the writings of the composer. When the composer and performer are combined, however, a unique situation arises: the link from the composer to the listener becomes a direct line and the composer becomes his/her own interpreter. Such is the case with Benjamin Britten. Britten conducted almost his entire repertoire in recordings for Decca (the exceptions being Paul Bunyan, Owen Wingrave, and Death in Venice). A comparative analysis of the recordings of four of Britten's works, the Serenade for Tenor, Horn, and Strings, Op. 31; Albert Herring, Op. 39; Spring Symphony, Op. 44; and the Nocturne, Op. 60, shows that despite his complaints about performers not following his tempo markings, Britten often deviated from them himself, tending slower. Britten also occasionally added additional rubato, ritardandi, and accelerandi to his works. Additionally, a discrepancy regarding a pitch in the "Prelude" of the Serenade comes to light. Video of Britten conducting the Nocturne in rehearsal with the Canadian Broadcasting Company (CBC) Vancouver provides additional insight into his methodology. Benjamin Britten succeeded as a composer-conductor, and his catalogue of recordings provides essential primary reference material when studying his 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 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)0.002
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.021
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.003

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 head0.004
GPT teacher head0.139
Teacher spread0.135 · 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
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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