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Record W1590400611 · doi:10.3138/tric.30.1_2.152

Building a Canadian Disability Arts Network: an Intercultural Approach

2009· article· en· W1590400611 on OpenAlexaffvenueabout
Kirsty Johnston

Bibliographic record

VenueTheatre Research in Canada · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRubricScholarshipThe artsDisability studiesMetaphorDiversity (politics)SociologyIdentity (music)PedagogyGender studiesPublic relationsPsychologyPolitical scienceAestheticsAnthropologyLawLinguistics

Abstract

fetched live from OpenAlex

A growing body of scholarship highlights the critical import of interdisciplinary dialogue between performance studies and disability studies. This dialogue seems particularly relevant for intercultural performance studies and in this essay I argue that an emerging Canadian disability arts presenters’ network offers a useful opportunity for such an exchange. The metaphor of the network also provides a model for thinking about intercultural creation in ways that favour ongoing dialogue and debate over consensus. Since its inaugural meeting in 2006, this network has drawn together pioneering presenters of disability arts and cultural events, each involving a significant theatre component, from across Canada. Canadians have shown considerable leadership in local and international disability arts and cultural movements but there has hitherto been little coordination between presenters or professional support on a national level. This network’s origins, membership, activities, and arguments shed light on diverse disability cultures at play in Canada and press intercultural performance studies to include disability as an important identity rubric while also attending to this diversity.

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)0.009
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0600.025
Scholarly communication0.0210.008
Open science0.0040.017
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.119
GPT teacher head0.331
Teacher spread0.212 · 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

Citations8
Published2009
Admission routes3
Has abstractyes

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