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Record W3112919978 · doi:10.3138/tric.41.2.01.en

Performance For/By/With Young People in Canada

2020· article· en· W3112919978 on OpenAlexaffvenueabout
Sandra Chamberlain-Snider, Heather Fitzsimmons Frey

Bibliographic record

VenueTheatre Research in Canada · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsMacEwan UniversityCanadian Association for Theatre ResearchUniversity of Victoria
Fundersnot available
KeywordsWitnessThe artsYoung personSociologyPsychologyAestheticsPublic relationsMedia studiesPolitical scienceLawDevelopmental psychologyArt

Abstract

fetched live from OpenAlex

This special issue examines the advocacy for and significance of discussing performance for/by/with young people in Canada. It asks how thinking about young people as audience members, creators, and co-creators can expose ideas about who they are, what they want, and what adults believe is good for them. The nineteen writers who contributed full-length articles and forum essays to this special issue demonstrate how attentive consideration to young people complicates creation ethics, aesthetic choices, affective impacts, content decisions, approaches to training, working conditions, and ideas about risk in connection to the performing arts. As the authors discuss how young people imagine, witness, train, and perform, they are simultaneously advocating for the young people they write about, for the specific issues that concern them, and for these perspectives to expand and invigorate broad conversations about Canadian performance for all ages.

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.081
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0270.006
Scholarly communication0.0120.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.064
GPT teacher head0.275
Teacher spread0.210 · 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
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

Citations1
Published2020
Admission routes3
Has abstractyes

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