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Record W2913102319 · doi:10.7202/1055433ar

Inuit-Centred Learning in the Inuit Bachelor of Education Program

2019· article· en· W2913102319 on OpenAlexaffvenueabout
Sylvia Moore, Cheryl Allen, Marina Andersen, Doris Boase, Jenni-Rose Campbell, T.J. Doherty, Alanna Edmunds, Felicia Edmunds, Julie Flowers, Jodi Lyall, Cathy Mitsuk, Roxanne Nochasak, Vanessa Pamak, Frank Russell, Joanne Voisey

Bibliographic record

VenueÉtudes/Inuit/Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsInuit Tapiriit KanatamiMemorial University of Newfoundland
Fundersnot available
KeywordsBachelorGeneral partnershipCurriculumPresentation (obstetrics)National curriculumGovernment (linguistics)PedagogyIdentity (music)SociologyPolitical scienceLibrary scienceGeographyArchaeologyMedicineLinguisticsArt

Abstract

fetched live from OpenAlex

The Inuit Bachelor of Education (IBED) program in Labrador is a partnership between the Nunatsiavut Government (NG) and Memorial University of Newfoundland. It is preparing teachers to be key participants in NG’s education system. The IBED students and Sylvia Moore, the lead faculty member in the program, have based this paper on a collaborative presentation. The writers explore the tensions between the current provincial curriculum offered in the regional schools and a curriculum that is founded on Inuit history, culture, and worldview, restores the central role of the Inuit language, and is community-based as recommended in the 2011 National Strategy on Inuit Education. The students discuss four key threads of culturally relevant education: land, language, resources, and local knowledge. Moore reflects on how the IBED program incorporates these same elements to support Inuit identity and the developing pedagogy of the pre-service teachers.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.930
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.004
Scholarly communication0.0060.003
Open science0.0020.010
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.041
GPT teacher head0.363
Teacher spread0.322 · 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

Citations3
Published2019
Admission routes3
Has abstractyes

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