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Record W2947485535 · doi:10.15402/esj.v5i2.68351

Audience Engagement in Theatre for Social Change

2019· article· en· W2947485535 on OpenAlexvenueno aff
Jessica Litwak

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDeliverablePublic engagementExperiential learningCommunity engagementSociologyAudience responseField (mathematics)Media studiesAction (physics)Audience participationPublic relationsSocial engagementPolitical sciencePedagogySocial scienceEngineering

Abstract

fetched live from OpenAlex

This report from the field describes some of the author’s methods of audience engagement as a means of social engagement, discussing the implications for practice. The report invites dialogue with the reader about the usefulness of audience engagement and ways it can be manifested before, during and after performance. Theatre is a vibrant and valuable tool for sparking dialogue and inspiring action around challenging social topics. Audiences who are engaged in the process of the performance beyond the standard role of passive spectator are more likely to be motivated to deliverable endeavors post performance. This report from the field offers four brief case studies as examples of audience engagement and includes pragmatic techniques for using theatre as a vehicle for personal and social change through audience engagement. It explores how artists can galvanize and empower audiences by creating experiential communities pre, during, and post-show. Drawing upon examples from high-quality international theatre projects written and directed by the author, the essay investigates and describes the work of The H.E.A.T. Collective including My Heart is in the East (U.S., U.K. and Europe), The FEAR Project (produced in the US, India and Czech Republic), Emma Goldman Day (U.S.).

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)0.665
metaresearch head score (Gemma)0.064
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6650.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.4320.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.512
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 head0.282
GPT teacher head0.392
Teacher spread0.110 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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