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Record W2765152112 · doi:10.1386/atr.5.2.67_1

Theatre and participation: Towards a holistic notion of participation

2017· article· en· W2765152112 on OpenAlexaff
Taiwo Afolabi

Bibliographic record

VenueApplied Theatre Research · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDramaThe artsSociologyTheatre studiesAudience participationAestheticsMedia studiesVisual artsArt

Abstract

fetched live from OpenAlex

Abstract Participation is central to and essentialized in theatre and interactive arts. While scholars have articulated the importance of participation in arts, participation has also been considered a foundational principle in development discourse, and it is largely external. Beyond the notion of participation as an external force, which I term a verb-oriented notion of participation, is the noun-oriented notion of participation, which is innate and organically induced from within an individual and a group or community (collective). In this article, I discuss a dual notion of participation and a relational interaction between these notions, which can lead to a holistic insight on participation. Using a case study that deals with managing conflict and bullying in a secondary drama classroom, an applied theatre project among refugees in Australia, I explore how this holistic insight into participation can influence how participation is framed and conceptualized in any applied theatre project. I argue that participation has been framed using a onesided, verb-oriented approach, and I propose a holistic notion of participation as a tool to rethink, reposition, reconceptualize and re-evaluate participation in applied theatre practice.

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)0.011
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.067
Scholarly communication0.0140.013
Open science0.0020.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.226
GPT teacher head0.412
Teacher spread0.186 · 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
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

Citations11
Published2017
Admission routes1
Has abstractyes

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