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Record W2626273645 · doi:10.1177/1177180117714406

Generating and sustaining positive spaces: reflections on an Indigenous youth urban arts program

2017· article· en· W2626273645 on OpenAlexafffundabout
Julian Robbins, Warren Linds, Benjamin Ironstand, Erin Goodpipe

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsInstitute of Population and Public HealthFirst Nations University of CanadaInstitute of Indigenous Peoples' Health
FundersCanadian Institutes of Health Research
KeywordsIndigenousGrassrootsKinshipThe artsColonialismSpace (punctuation)SociologyGender studiesPolitical scienceAnthropologyLawEcology

Abstract

fetched live from OpenAlex

Life in the city for any youth can be challenging without a proper support network. For Indigenous youth in particular, the unique burden of intergenerational trauma due to the residual effects of colonialism (e.g. residential schools and historical outlawing of traditional practices) can contribute to both unhealthy behaviors and a continuation of “culturally unsafe” spaces. As a response to these challenges, this article examines the positive effects that a grassroots film creation and production program in a major urban centre in Saskatchewan, Canada, had on participating Indigenous youth. Community-based researchers from the Indigenous Peoples’ Health Research Centre observed how a culturally safe space created the conditions to enable youth to become creative through the arts in an environment supported by an intergenerational network of Nêhiyaw (Plains Cree) kinship relationships called wâhkôtowin. The article also argues that the effectiveness of culturally safe spaces can benefit from recognizing the operation of ethnogenetic processes in contemporary environments.

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)0.005
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: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0280.013
Scholarly communication0.0060.003
Open science0.0020.012
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.415
Teacher spread0.365 · 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
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueAlterNative An International Journal of Indigenous PeoplesSame topicIndigenous Health, Education, and RightsFrench-language works237,207