The Role of Inuit Languages in Nunavut Schooling: Nunavut Teachers Talk about Bilingual Education
Bibliographic record
Abstract
This article provides a discourse analysis of interview transcripts generated from 10 experienced Nunavut teachers (five Inuit and five non‐Inuit) regarding the role of Inuit languages in Nunavut schooling. Discussion and analysis focus on the motif of bilingual education. Teachers’ talk identified discourse models of “academic truths” and “revitalization,”demonstrating how Nunavut teachers are making efforts to en‐ gage with community to effect lasting educational change. Key words: Aboriginal languages; Nunavut education, language policy, discourse analysis, educational change Cet article présente une analyse de discours à partir de transcriptions d’entrevues auprès de dix enseignantes d’expérience du Nunavut (cinq Inuits et cinq non‐Inuits) au sujet du rôle des langues inuites dans les écoles du Nunavut. Les discussions et analyses portent sur la raison d’être de l’enseignement bilingue. Dans leurs propos, les enseignantes ont identifié des modèles discursifs des « vérités pédagogiques » et de la « revitalisation », démontrant par là comment le personnel enseignant au Nuna‐ vut s’efforcent de travailler de concert avec la communauté pour favoriser des chan‐ gements à long terme dans l’éducation. Mots clés : langues autochtones, éducation au Nunavut, politiques linguistiques, analyse de discours, changement en l’éducation.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".