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Works and Performances in the Performing Arts

2009· article· en· W2034592236 on OpenAlexaff
David Davies

Bibliographic record

VenuePhilosophy Compass · 2009
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerforming artsImprovisationThe artsDanceAestheticsVisual artsQuality (philosophy)ArtComputer sciencePsychologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract The primary purpose of the performing arts is to prepare and present ‘artistic performances’, performances that either are themselves the appreciative focuses of works of art or are instances of other things that are works of art. In the latter case, we have performances of what may be termed ‘performed works’, as is generally taken to be so with performances of classical music and traditional theatrical performances. In the former case, we have what may be termed ‘performance‐works’, as, for example, in free improvisations. Where we have performances of performed works, a number of distinctive philosophical questions arise: What kind of thing is a performed work? How is it appreciated through its performances? Is ‘authenticity’ an artistically relevant quality of performances of performed works, and, if so, why? How much of what goes on in the performing arts is rightly viewed as the performance of performed works? Artistic performances, whether or not they are of performed works, raise philosophical questions of their own. Can a performance itself be rightly viewed as a work of art? How do improvisation and rehearsal enter into the performing arts, and how do they bear on the appreciation of artistic performances? What role does the audience play in such performances? Does the performer’s use of her own body as an artistic medium, as for example in dance performance, generate special constraints on appreciation? How, finally, does what is usually classified as ‘performance art’ relate to activities in the performing arts more generally construed? I critically survey the ways in which these questions have been addressed by principal theorists in the field.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.033
Scholarly communication0.0110.005
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.002

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 head0.069
GPT teacher head0.326
Teacher spread0.257 · 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 designTheoretical or conceptual
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

Citations9
Published2009
Admission routes1
Has abstractyes

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