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Record W2437448373 · doi:10.37119/ojs2016.v22i1.262

Kina’muanej Knjanjiji’naq mut ntakotmnew tli’lnu’ltik (In the Foreign Language, Let us Teach our Children not to be Ashamed of Being Mi’kmaq)

2016· article· en· W2437448373 on OpenAlexafffundvenue
Ashley Julian, Ida Denny

Bibliographic record

Venuein education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousCurriculumForeign languageFirst languageIndigenous languagePedagogyNarrativeSociologyEnglish languageLinguisticsPsychologyArtMathematics educationLiteratureBiologyPhilosophy

Abstract

fetched live from OpenAlex

Colonialism has assimilated and suppressed Indigenous languages across Turtle Island ( North America). A resurgence of language is needed for First Nation learners and educators and this resurgence is required if Indigenous people are going to revitalize, recover and reclaim Indigenous languages. The existing actions occurring within Indigenous communities contributing to language resurgence include immersion schools. Eskasoni First Nation opened its doors in September 2015 to a full immersion school separate from the English speaking educational centers. This move follows the introduction of Mi'kmaq immersion over ten years earlier within the English speaking school in the community. The Mi’kmaw immersion school includes the Ta’n L’nuey Etl-mawlukwatmumk Mi’kmaw Curriculum Development Centre that assists educators in translating educational curriculum from the dominant English language to Mi’kmaq. In this paper, stories are shared about the Eskasoni immersion program’s actions towards language resurgence through a desire-based lens, based on rich narratives from three Mi’kmaw immersion educators.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.340
Teacher spread0.325 · 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.

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

Citations2
Published2016
Admission routes3
Has abstractyes

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