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Record W4379141256 · doi:10.3138/tric-2022-0017

Soulpepper 2022: Decolonizing Toronto Theatre

2023· article· en· W4379141256 on OpenAlexaffvenueabout
Hanna Shore

Bibliographic record

VenueTheatre Research in Canada · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsIndigenousThe artsMainstreamEthnographyVisual artsMedia studiesPerforming artsSociologyNarrativeDecolonizationPoliticsInclusion (mineral)PuppetryArtGender studiesPolitical scienceAnthropologyLiterature

Abstract

fetched live from OpenAlex

This research project, “Decolonizing Toronto Theatre,” examines how Soulpepper, a mainstream Toronto theatre company, and their collaboration with Native Earth Performing Arts are contributing to the equity, diversity, inclusion, and decolonization of Toronto theatre through their recent Indigenous productions: Kamloopa and Where the Blood Mixes. The author watched, read, and analyzed both plays to explore how these two productions transform and redefine the intellectual, political, and artistic conventions of Anglo-Canadian theatre. Her analyses of these plays are informed by the various texts centered around Canadian Indigenous history and Indigenous theatre. She also used an ethnographic approach by talking to people involved in both productions. She conducted interviews with the playwrights, the associate artistic director at Soulpepper, and some artists involved in both plays. These conversations with the people involved allowed her to understand these plays beyond their content: the inner workings of how a production comes to fruition. The conversations also allowed for a reflection on the similarities and differences between the creative approaches the artists involved took as well as the positive impacts these productions have had on Toronto theatre. Finally, by applying ethnographic findings and analyses of the plays, this piece compiles the analyses and research conducted over the course of the internship.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.097
GPT teacher head0.329
Teacher spread0.232 · 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 designNot applicable
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

Citations0
Published2023
Admission routes3
Has abstractyes

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