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Record W4245534987 · doi:10.3138/ctr.177.005

<i>In Sundry Languages</i>

2019· article· en· W4245534987 on OpenAlexvenueaboutno aff

Bibliographic record

VenueCanadian Theatre Review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsImprovisationRealmAestheticsConversationComedyNothingJokeArtPolitenessRefugeeVisual artsSociologyLiteratureLinguisticsHistoryCommunication

Abstract

fetched live from OpenAlex

An imaginative space where culturally and linguistically diverse artists can come together to experiment with various modes of theatrical expression, “In Sundry Languages” does not tell one story in a linear fashion. Instead of recreating stories on stage verbatim, it subverts reality by placing real stories in a realm of the seemingly impossible and conspicuously theatrical. It uses a multiplicity of theatre languages and genres: a soft-shoe routine, elements of tanztheater, improvisation with audiences, dramatic scenes, live videos, monologues, songs, poetry, and multimedia. It plays with the inappropriate and the ‘uncomfortable’ asking the audience to constantly keep biases in check or think twice before laughing (or crying) over the show’s multilingual encounters. An immigrant actor keeps auditioning and fails to get any roles, unless taking roles ridiculing his own culture, language or accent. A refugee tries to establish a connection with locals but fails to penetrate the iconic and often superficial Canadian ‘politeness.’ A meeting of two recent newcomers to Canada results in a major misunderstanding and frustration as both reveal opposing beliefs and convictions. A phone sex conversation performed in the dark explores how the language of the ‘Other’ can become nothing but a fetish. Each multilingual encounter ends in an impasse when the Otherness refuses to be harnessed.]

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.002

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 head0.027
GPT teacher head0.266
Teacher spread0.240 · 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 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
Published2019
Admission routes2
Has abstractyes

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