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Record W3011850282 · doi:10.3138/tric.40.1_2.42

Art for Everyone? Mush, Multiculturalism, and the Prismatic Arts Festival

2019· article· en· W3011850282 on OpenAlexafffundvenueabout
Brittany Kraus

Bibliographic record

VenueTheatre Research in Canada · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsCanadian Association for Theatre ResearchUniversity of Toronto
FundersCanada Council for the Arts
KeywordsMainstreamThe artsNegotiationMulticulturalismIndigenousVisual artsSociologyMedia studiesArtPolitical scienceSocial scienceLawPedagogy

Abstract

fetched live from OpenAlex

Founded in 2008 by Shahin Sayadi and Maggie Stewart, the Prismatic Arts Festival is a Halifax-based multidisciplinary arts festival that features the work of Indigenous and culturally diverse artists. This article examines the development of the Prismatic Arts Festival and the ways in which the festival has sought to negotiate, challenge, and transform Halifax’s artistic landscape by creating a model that is locally-grounded, nationally-networked, and fundamentally devoted to advancing the careers and profiles of Indigenous and culturally diverse artists in Nova Scotia and across Canada both within and outside of mainstream performance cultures. As the festival recently celebrated its tenth anniversary, this article traces the history of the Prismatic Arts Festival, its struggles and successes, and the complex negotiations the festival has made and continues to make in order to move toward a future of Canadian theatre in which cultural diversity and inclusivity are the norm, rather than the exception.

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)0.004
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.071
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0320.031
Scholarly communication0.0140.003
Open science0.0010.010
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.070
GPT teacher head0.298
Teacher spread0.228 · 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

Citations0
Published2019
Admission routes4
Has abstractyes

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