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Record W2507172313 · doi:10.7202/1036454ar

Danse en tandem : étude du mouvement des spectateurs et des performeurs dans Sleep No More de Punchdrunk1

2016· article· fr· W2507172313 on OpenAlexvenueno aff
Julia Ritter

Bibliographic record

VenueTangence · 2016
Typearticle
Languagefr
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

La compagnie britannique Punchdrunk a créé plusieurs productions théâtrales immersives depuis sa fondation en 2000. Sleep No More se distingue de celles-ci puisqu’on y présente le drame de Macbeth de Shakespeare par le truchement de la danse, mais aussi en raison du succès critique et commercial du spectacle. À l’aide des explications de Maxine Doyle, chorégraphe et directrice artistique associée de Sleep No More et des réflexions des performeurs et des spectateurs de la version new-yorkaise, la chercheuse en danse Julia M. Ritter émet l’hypothèse que la popularité de ce théâtre immersif est due en grande partie à la manière dont la danse est conçue et s’avère centrale comme méthode destinée à faciliter l’expérience du spectateur. Selon elle, Sleep No More fonctionne sur le modèle d’une danse en tandem entre les membres de la distribution et les spectateurs. La danse serait le médium structurant du contenu interprété par les danseurs professionnels et elle se déploierait par le biais d’improvisations susceptibles d’inciter les spectateurs à s’y mouvoir. L’auteure montre comment la danse exécutée dans Sleep No More permet au public de transformer en danse son expérience de spectateur tout en lui offrant des moments de découvertes personnelles et en l’amenant à développer une agentivité créative qui le conduit à osciller entre les rôles de participant, créateur, spectateur, curateur et performeur.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.011
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.001

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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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 designQualitative
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

Citations14
Published2016
Admission routes1
Has abstractyes

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