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Record W3167265578 · doi:10.3138/tric.42.1.f01

Radical Refusals and Indigenous Gifts of Love: A Conversation on Indigenous Theatre after <i>bug</i>

2021· article· fr· W3167265578 on OpenAlexaffvenueabout
Cole Alvis, Yolanda Bonnell, Kim Senklip Harvey, Lindsay Lachance, Sheetala Bhat

Bibliographic record

VenueTheatre Research in Canada · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWestern UniversityUniversity of British Columbia
Fundersnot available
KeywordsHumanitiesIndigenousEthnologyArtPolitical scienceSociology

Abstract

fetched live from OpenAlex

En février 2020, le collectif manidoons a exprimé le souhait que la reprise de la pièce bug, co-présentée par le Théâtre Passe Muraille et la compagnie Native Earth Performing Arts, soit critiquée uniquement par des personnes autochtones, noires ou de couleur (PANDC). bug est un spectacle solo écrit et interprété par l’artiste sud-asiatique et ojibwée Yolanda Bonnell, mis en scène par l’artiste métis Cole Alvis et créé par le collectif manidoons. La décision du collectif a suscité une polémique importante. Le 27 avril 2020, Cole Alvis, Yolanda Bonnell, la dramaturge et metteure en scène Kim Senklip Harvey (Syilx, Tsilhqot’in, Ktunaxa et Dakelh) et la chercheure et conseillère dramaturgique Lindsay Lachance (Algonquine anishinaabe) ont participé à une discussion en ligne animée et documentée par Sheetala Bhat, doctorante à la Western University, sur les pratiques théâtrales autochtones, les attentes du grand public en ce qui concerne la critique des œuvres théâtrales, les approches décoloniales et autres à l’acte d’assister à une pièce de théâtre autochtone et d’y réagir. Les personnes ont également abordé d’autres préoccupations qui touchent les PANDC qui font du théâtre.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0350.039
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0030.011
Insufficient payload (model declined to judge)0.0060.001

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 head0.032
GPT teacher head0.337
Teacher spread0.305 · 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 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

Citations4
Published2021
Admission routes3
Has abstractyes

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Same venueTheatre Research in CanadaSame topicIndigenous Health, Education, and RightsFrench-language works237,207