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Record W7074107267

Reviving Haydn new appreciations in the twentieth century

2015· article· en· W7074107267 on OpenAlexaboutno aff

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReputationPeriod (music)MusicalTurning pointQuarter (Canadian coin)Event (particle physics)Music historyPerformance art
DOInot available

Abstract

fetched live from OpenAlex

By the 1840s Joseph Haydn, who died in 1809 as the most celebrated composer of his generation, had degenerated into the bewigged "Papa Haydn," a shallow placeholder in music history who merely invented the forms used by Beethoven. In a remarkable reversal, Haydn swiftly regained his former stature within the opening decades of the twentieth century. Reviving Haydn: New Appreciations in the Twentieth Century examines both the decline and the subsequent resurgence of Haydn's reputation in an effort to better understand the forces that shape critical reception on a broad scale. No single person or event marked the turning point for Haydn's reputation. Instead a broad resurgence reshaped opinion in Europe and the United States in short order. The Haydn revival engaged many of the music world's leading figures -- composers (Vincent d'Indy and Arnold Schoenberg), conductors (Arturo Toscanini), performers (Wanda Landowska), critics (Lawrence Gilman), and scholars (Heinrich Schenker and Donald Tovey) -- each of whom valued Haydn's music for specific reasons and used it to advance particular goals. Yet each advocated for a rehearing and rereading of the composer's works, calling for a new appreciation of Haydn's music. Bryan Proksch is Assistant Professor of Music History at Lamar University.

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)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.011
Scholarly communication0.0110.007
Open science0.0010.009
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.064
GPT teacher head0.215
Teacher spread0.151 · 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
Published2015
Admission routes1
Has abstractyes

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