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Record W2957447604 · doi:10.5539/res.v11n3p26

Recreating an American Myth: An Analytical Reading of Paul Bunyan by W. H. Auden and Benjamin Britten

2019· article· en· W2957447604 on OpenAlexvenueno aff

Bibliographic record

VenueReview of European Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAllegianceMythologyMusicalReading (process)NarrativeSociologyIdentity (music)Order (exchange)Cultural identityAestheticsLiteratureArtLawSocial sciencePolitical science

Abstract

fetched live from OpenAlex

One of the ability which music possesses is to evoke the audience’s sense of cultural and national identity. In the second half of the twentieth century, people can easily travel and relocate to a new country in order to search for a better living condition. However, with this newly found freedom, people’s sense of belonging and cultural identity has been put into serious doubts and tests. W. H. Auden and Benjamin Britten’s Paul Bunyan encapsulates and foresees this phenomenon. Both have just arrived at the United States to escape from the war-torn Britain. Eager to find a voice to suit their new audience and symbolically swear allegiance to their newly adopted country, Auden and Britten employed an American founding myth in order to engage with their American patrons. Through a closed reading of Paul Bunyan, listeners will soon realize the inseparable notions between the musical presentation and its cultural identity. Furthermore, the story is told in the form of American musical theater, which is the artists’ ambitious attempt to capture the American optimism and spirit. This article intends to explore the notion of “myth narrative” in the genre of Music Theater; and how it reflects both the poet and the composers’ intention to obtain their sense of American identity.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0210.019
Scholarly communication0.0120.009
Open science0.0010.005
Research integrity0.0030.011
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.324
Teacher spread0.265 · 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
Published2019
Admission routes1
Has abstractyes

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