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Record W3020434973 · doi:10.1111/maq.12575

Performing <i>Pimâtisiwin</i>: The Expression of Indigenous Wellness Identities through Community‐based Theater

2020· article· en· W3020434973 on OpenAlexaffabout
Andrew R. Hatala, Kelley Bird‐Naytowhow

Bibliographic record

VenueMedical Anthropology Quarterly · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousPerformative utteranceIdentity (music)SociologyPsychological resilienceGender studiesThe artsSocial identity theoryExpression (computer science)Representation (politics)Media studiesSocial psychologyVisual artsPsychologyAestheticsSocial scienceSocial groupPolitical scienceArtEcology

Abstract

fetched live from OpenAlex

The performing arts can be a powerful means of wellness, identity exploration, and positive social representation for Indigenous young people. In this article, we outline the results of a year-long collaborative study that explored Indigenous young peoples' relationships between the performing arts, wellness, and resilience. Twenty in-depth interviews were conducted with 10 Cree and Métis youth about their participation in the Circle of Voices theater program at the Gordon Tootoosis Nik̄an̄iw̄in Theatre in Saskatoon, Saskatchewan, Canada. A strength-based analysis focused on performing pimâtisiwin, that is, how young people learn to enact, protest, and play with a wide range of social identities, while also challenging racially stereotyped identities often imposed on them within inner-city environments. This research critically engages performative theory to more readily understand aspects of Indigenous youth identity and wellness and offers new empirical and methodological directions for advancing Indigenous youth wellness in urban settings.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.004
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.051
GPT teacher head0.300
Teacher spread0.250 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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