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Record W3112405933 · doi:10.1002/jcop.22481

Indigenous youth reconnect with cultural identity: The evaluation of a community‐ and school‐based traditional music program

2020· article· en· W3112405933 on OpenAlexafffund
Arla Good, Lori Sims, Keith Clarke, Frank Russo

Bibliographic record

VenueJournal of Community Psychology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsYukon UniversityYukon Department of EducationYukon Health and Social ServicesSelkirk CollegeYukon Department of EnvironmentToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousCultural identityPositive Youth DevelopmentSociologyDancePsychologyPedagogySocial scienceDevelopmental psychologyVisual arts

Abstract

fetched live from OpenAlex

Reconnecting Indigenous youth with their cultural traditions has been identified as an essential part of healing the intergenerational effects of forced assimilation policies. Past work suggests that learning the music of one's culture can foster cultural identity and community bonding, which may serve as protective factors for well-being. An 8-week traditional song and dance program was implemented in a school setting for Indigenous youth. An evaluation was conducted using a mixed-method design to determine the impact of the program on 35 youth in the community. A triangulation of qualitative and quantitative data revealed several important themes, including personal development, cultural development, social development, student engagement in school-based programming, and perpetuating cultural knowledge. The program provided students with an opportunity to connect with their cultural traditions through activities that encouraged self and cultural expression. Community responses suggested that this type of programming is highly valued among Indigenous communities.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.324
GPT teacher head0.440
Teacher spread0.117 · 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 designObservational
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

Citations36
Published2020
Admission routes2
Has abstractyes

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Same venueJournal of Community PsychologySame topicIndigenous Health, Education, and RightsFrench-language works237,207