Inuit principals and the changing context of bilingual education in Nunavut
Bibliographic record
Abstract
Although positive policies and laws promote the Inuit language andInuit qaujimajatuqangit(IQ) in all sectors of Nunavut society, including at all levels of Inuit schooling, many Nunavut schools are still struggling to overturn colonizing practices and mindsets that have hindered effective education of Inuit youth. In this article, we document perceptions of students, teachers, principals, parents, and community members related to school transformation under the leadership of an Inuk principal and and Inuk co-principal in two Nunavut high schools. These oral accounts show that having an Inuit principal enhanced students’ opportunities to learn and practise the Inuit language and IQ through enhanced, localized programming and increased exposure to Inuit ways of speaking and being. Parents were mobilized and equipped to support and advocate for their children, including joining local District Education Authorities, when they were able to communicate easily and effectively with the principal, and saw their knowledge, culture, and language valued and practised in the school system. We argue that the strong, community-anchored leadership modelled in these two schools transformed the context for effective intercultural, bilingual education. Results point to the importance of leadership by school principals in actualizing the goals set out in Nunavut’s Education Act (2008), governmental mandates, and language laws.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".