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Record W2768622995 · doi:10.1121/1.5014811

Multi-purpose halls… Wave of a new future or dead-end?

2017· article· en· W2768622995 on OpenAlexaboutno aff
Paul H. Scarbrough, Christopher Blair

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsSymphonyFlexibility (engineering)BalletDramaOperaOpera houseVisual artsBridge (graph theory)Performing artsDanceArchitectural engineeringHistoryManagementArtArt historyEngineering

Abstract

fetched live from OpenAlex

Multi-purpose halls, that is, performance spaces that can adapt their acoustical and technical features to serve many different performance types are a uniquely North American invention. Europe, where the modern-day symphony orchestra, opera company, ballet company, and drama troupe all originated, favors halls that are purpose-built to serve each art form. In the United States and Canada, even in cities that could afford to field multiple ensembles or companies, building dedicated facilities for each art form was often a bridge too far. This gave rise to the multi-purpose hall and with it the old saw that multi-purpose meant no-purpose. The success of new venues in Charleston, San Antonio, León, México, and elsewhere demonstrates that multi-purpose halls are cost-effective solutions and that they need not be the compromises they once were. The authors will explore the development of multi-purpose halls starting with Louis Sullivan’s Auditorium Theater in Chicago, arguably the forerunner of the contemporary multi-purpose hall, through to the current day. The authors will also explore how the evolving creative impulses of artists and directors is challenging designers to go even further, and incorporate degrees of flexibility into these spaces that would have been unimaginable just two decades ago.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.227

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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