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Record W2761748083 · doi:10.1386/peet.7.1.25_1

Delegated performance: Interdisciplinary tensions, provocations and questions

2017· article· en· W2761748083 on OpenAlexaff
Monica Prendergast

Bibliographic record

VenuePerforming Ethos International Journal of Ethics in Theatre and Performance · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDramaDelegationCitizen journalismPerformance practicePerformance studiesSet (abstract data type)SociologyValue (mathematics)Performance managementPedagogyPsychologyVisual artsManagementArtPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

Abstract Art historian and critic Claire Bishop and theatre/performance scholar Jen Harvie have each written about a relatively new form of socially engaged participatory performance art that Bishop calls ‘delegated performance’. Delegated performance differs from earlier forms of performance art in that the performance is delegated to other people (hired or volunteer), including (at times) spectators, who enact the piece, rather than the piece being carried out by the artist herself. This set of poetically informed microlectures examines delegated performance in the attempt to glean interdisciplinary understandings about how the delegation of performance happens in drama education and applied theatre. What might those who work and research in these fields consider as issues surrounding the delegating performance? Of what value is this notion in assisting in thinking about practice? Examples of delegated performance are drawn from Bishop and Harvie and additional sources and considered for the ethical questions and provocations they raise. In light of this investigation, drama educators and applied theatre facilitators are recommended to consider how much and in what ways they are delegating performance with students/participants, and to what effect.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.364
Teacher spread0.290 · 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 designObservational
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

Citations4
Published2017
Admission routes1
Has abstractyes

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