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Record W2806953938 · doi:10.7939/r38g8fp30

Intracultural theatre in Canada: Rahul Varma's 'Counter Offence' and 'Bhopal'

2016· article· en· W2806953938 on OpenAlexaboutno aff
Rohan Kulkarni

Bibliographic record

VenueUniversity of Alberta Library · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLawHistoryPolitical scienceSociology

Abstract

fetched live from OpenAlex

Canadian theatre has always been an exercise in inter-cultural negotiation, yet in the past few decades, the official multicultural legislation has provided opportunities for more artists belonging to ethnic minorities to consciously diversify our country’s theatre practice. Montreal based Indo-Canadian playwright Rahul Varma has been a leading figure in creating intracultural theatre, which seeks to question the discourse of multiculturalism. Along with his company Teesri Duniya Theatre, whose mandate is to produce socially and politically minded theatre that reflects Canada’s diversity, Varma has staged plays such as Counter Offence and Bhopal in order to create counter-discursive spaces where the audience may examine ‘benign’ forces such as multiculturalism and globalization. These two plays are situation-based dramas where various socio-political issues collide and conflict, allowing the audience to witness multiple points of view and understand the tensions, power dynamics, and inequalities inherent in negotiations between cultures.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.047
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0280.011
Scholarly communication0.0100.001
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.006
GPT teacher head0.136
Teacher spread0.130 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2016
Admission routes1
Has abstractyes

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