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Record W3212675268 · doi:10.33137/tijih.v1i2.36047

Urban Indigenous Second-Language Learning

2021· article· en· W3212675268 on OpenAlexafffundabout
Josha Rafael

Bibliographic record

VenueTurtle Island Journal of Indigenous Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousIndigenous languageThematic analysisExploratory researchTraditional knowledgeFirst languageSociologyGeographyLinguisticsQualitative researchSocial scienceEcology

Abstract

fetched live from OpenAlex

As the language revitalization movement progresses, the impacts of Indigenous language beyond the domain of language use are gaining recognition. Previous literature has identified links between Indigenous language revitalization efforts and Indigenous well-being, but to date, there are few studies that explore this topic thoroughly. The purpose of this study is to explore the impacts of Indigenous second-language learning on urban Indigenous perceptions of their well-being. Urban Indigenous populations are growing, and are particularly impacted by language loss; thus, it is vital that urban perspectives be represented. Indigenous language-learners from the Nêhiyaw (Cree) Language Lessons Program in Edmonton, Alberta participated in semi-structured interviews. Five exploratory themes emerged from thematic analysis. The results presented in this paper, while exploratory, are a meaningful addition to the existing scholarship in this field. They may be used as a departure point for future research on the topic of Indigenous language learning and how it impacts Indigenous well-being.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.419
Teacher spread0.389 · 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 teacher head, not a consensus.

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

Citations0
Published2021
Admission routes3
Has abstractyes

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