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Record W1768909961 · doi:10.5860/choice.38-3249

A season of opera: from Orpheus to Ariadne

2001· article· en· W1768909961 on OpenAlexaboutno aff

Bibliographic record

VenueChoice Reviews Online · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsOperaArtHistoryLiteratureAesthetics

Abstract

fetched live from OpenAlex

Father Owen Lee is internationally known for his intermission commentaries featured during the Saturday afternoon broadcasts from the Metropolitan Opera House in New York. A Season of Opera: From Orpheus to Ariadne gathers together for the first time Father Lee's best broadcast and cassette commentaries, public lectures, and articles on twenty-three works for the musical stage. The essays range from the pioneering Orpheus of Monteverdi to the forward-looking Ariadne of Richard Strauss. Included are Father Lee's famous discussions of Mozart's Magic Flute and Beethoven's Fidelio, Verdi's La Traviata and Falstaff, Wagner's Tristan und Isolde, and Poulenc's Dialogues of the Carmelites. The concluding chapter, originally published as the lead article in The Opera Quarterly's special issue on the end of the twentieth century, is a thought-provoking forecast of opera's future. Recommendations for further reading, CD recordings, and videos are also included. Opera Canada has applauded Father Lee's 'extraordinary ability to engage, challenge, and enlighten a vast and diverse audience' and called his learning-worn-lightly commentaries 'a unique mix of spiritual empathy, classical scholarship, and psychological insight.' Opera lovers, or anyone interested in psychology and mythology, humanities and comparative literature, or the art of the essay will welcome this book.

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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0610.011

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 head0.134
GPT teacher head0.324
Teacher spread0.190 · 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
GenreReview

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

Citations5
Published2001
Admission routes1
Has abstractyes

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