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After the “Golden Age”

2012· book-chapter· en· W2131661015 on OpenAlexaboutno aff
Jessica Sternfeld, Elizabeth L. Wollman

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldArts and Humanities
TopicTheater, Performance, and Music History
Canadian institutionsnot available
Fundersnot available
KeywordsMusicalSpectacleEntertainmentSound (geography)Production (economics)AestheticsArtHistoryVisual artsPolitical scienceEconomicsLawAcoustics

Abstract

fetched live from OpenAlex

Abstract The article focuses on some of the more important developments that have affected the American musical over the years. The amount of money needed to produce a musical has increased since the onset of the depression, but especially since the 1960s. The cost of production, coupled with the introduction of several cheaper, more widely accessible entertainment forms, has forced the musical to struggle financially and aesthetically at various periods during the postwar era. Periods of high inflation, such as during the 1930s, affected the criteria for hit status, for example shows had to run for longer stretches to be profitable. The marketing for the show at that time was particularly intense, and the spectacle aspect was strongly promoted by producer Garth Drabinsky, under the auspices of his Canadian production company, Livent. The longer average runs of Broadway musicals depended in part on an increasingly international audience, which was seen as transitory and ever renewing. Since the advent of rock, amplification has become increasingly common and this was for several reasons. Many actors needed microphones to protect their voices and to be heard above the electric instruments that accompanied them. Film and sound recording technologies exerted significant influence on the stage musical and advances in sound design have allowed theatrical productions to offer cleaner, more balanced sound from the orchestra pit and stage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.974
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.167
Teacher spread0.136 · 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 teacher head, not a consensus.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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