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Record W2290610439 · doi:10.7202/1034611ar

L’exposition de la performance, entre reenactment et tableau vivant

2016· article· en· W2290610439 on OpenAlexaffvenue
Mélanie Boucher

Bibliographic record

VenueMuséologies Les cahiers d études supérieures · 2016
Typearticle
Languageen
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsNational Museum of Fine Arts of QuebecCégep de l'OutaouaisUniversité du Québec à Montréal
Fundersnot available
KeywordsExposition (narrative)ExhibitionRigourPresentation (obstetrics)ExemplificationArt historyArtSpace (punctuation)Visual artsSociologyHumanitiesComputer scienceEpistemologyPhilosophyLiteratureMedicine

Abstract

fetched live from OpenAlex

Expositions that consist of presentation of performances have an interest in generating a series of questions on the types of performances that are favourable for occupying space and time. What are they? This article retraces two types, re-enactment and the tableau vivant, and presents their main characteristics. These types of performances are prone to induce modifications in exhibition practices, and through that, raise questions regarding the relationship between research and creation. The cases, Seven Easy Pieces (2005), by Marina Abramovic at the Solomon R. Guggenheim, and An Immaterial Retrospective of the Venice Biennale (2013), by Alexandra Pirici and Manuel Pelmus at the Venice Biennale, serve initially as exemplification, then as epistemic presentation of the two types of performances considered. Through strategies however comparable, Seven Easy Pieces and An Immaterial Retrospective of the Venice Biennale presume diverse purposes. The first work would have as objective, iconization, by relying on the presence of the artist, as well as on the rigour of the document research. The second work offers an example of the critical use of the exposition, by exploiting a research that is not neither put forth nor supported.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.545

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 communication0.0010.002
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 head0.062
GPT teacher head0.276
Teacher spread0.214 · 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 designTheoretical or conceptual
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

Citations1
Published2016
Admission routes2
Has abstractyes

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