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The Niichii Project: Revitalizing Indigenous Language in Northern Canada

2021· article· en· W4226378192 on OpenAlexaffabout
Shelley Stagg Peterson, Yvette Manitowabi, Jacinta Manitowabi

Bibliographic record

VenueTESOL in Context · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousExperiential learningContext (archaeology)PedagogyProject commissioningSociologyIndigenous languagePublishingCultural learningLearning communityEcologyGeographyArchaeologyPolitical science

Abstract

fetched live from OpenAlex

Two Anishnabek kindergarten teachers discuss four principles of Indigenous pedagogies in a project with a university researcher that created a context for children to engage in activities to learn their Anishnabek language and culture, and create positive identities. The university researcher sent a rabbit puppet named Niichii (Friend), who was assigned the role of an Anishnaabek child whose family was from their Indigenous community but had moved away. Taking the role of Niichii’s Kokum (Grandmother), the university researcher asked the child to teach Niichii the community’s language and traditional ways. The teachers describe and interpret the learning activities of the Niichii project in terms of four elements of Indigenous pedagogies: intergenerational learning; experiential learning; spiritual learning involving interconnections with the land; and learning about relationality. Implications for other bilingual and multilingual contexts include creating role play contexts where children are positioned as teachers and helpers to support an imaginary character’s language and cultural learning, building on children’s funds of knowledge and highlighting cultural connections to the local community.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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
Published2021
Admission routes2
Has abstractyes

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