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Record W1585537967

Applied theatre as a decolonizing research method

2014· article· en· W1585537967 on OpenAlexaffabout
Anjali Helferty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousSociologySolidarityContext (archaeology)PedagogyVisual artsPolitical scienceArtHistoryPolitics
DOInot available

Abstract

fetched live from OpenAlex

This poster examines the possibilities for theatre of the oppressed as a decolonizing research methodology. For my doctoral research, I plan to engage Indigenous and non-Indigenous (settler) tar sands activists in Canada in a theatre of the oppressed workshop, in order to determine the potential use of this applied theatre methodology in building solidarity in an activist context. In undertaking this work, it is critical that I thoroughly examine my proposed methodology to ensure that I am undertaking decolonizing research. This poster presents the results of an initial investigation into theatre of the oppressed as a decolonizing research methodology in a social movement setting. This poster will contribute to research in social movement learning, Indigenous studies, and applied theatre.

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)0.047
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0090.022
Scholarly communication0.0120.006
Open science0.0040.015
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0300.004

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 head0.093
GPT teacher head0.360
Teacher spread0.267 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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
Published2014
Admission routes2
Has abstractyes

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