A Case Study of a Pilot Anishinaabemowin (Ojibwe) Immersion School on Manitoulin Island
Bibliographic record
Abstract
Canada’s education system has attempted to completely erase Indigenous languages in Canada and to replace them with English. However, work is being done to revitalize Anishinaabemowin (Ojibwe language). An example of this is the Mnidoo Mnising Anishinaabek Kinoomaage Gamig (MMAK) immersion program on Manitoulin Island, which operated for five years (2013–2018) as a test pilot education model. It was the first of its kind in the province due to its unique pedagogical approach: teaching through language and culture. This mixed-methods case study of the MMAK examines student success and community perspectives. This study has been analyzed through the lens of a Tribal Critical Race (TribalCrit) theoretical framework (Brayboy, 2005), which helped to make sense of both the nuances of Indigenous education and the complexities of anti-oppression and anti-racism. As well, a Participatory Action Research (PAR) approach was utilized to ensure it was community led and ultimately beneficial for the community. There were main priorities for the families who participated in the five-year study identified. These priorities were comprised in three key areas of immersion: student achievement, language fluency, and personal self-esteem. The first part of this study focuses on reviewing secondary statistical data for students in those three areas. The five-year results reveal the benefits of immersion on children ages four to ten years old in this unique, culturally relevant learning environment. The second part of this study examines the perspectives of families, communities, and school staff based on focus groups and face-to-face interviews on immersion education and the future of Indigenous education. From the focus groups and interviews, three overarching themes were identified as central points. First, languages are learned most effectively when taught both in the home and at school. Second, the essential learning outcomes in Indigenous education must include holistic learning, child development, and cultural revitalization. Third, as to what constitutes quality education, utilizing culturally relevant instructional styles and curricula to promote holistic learning is the cornerstone to culturally appropriate First Nation education. This study suggests that, in addition to having positive benefits for language revitalization, culture-based immersion education produces positive academic outcomes for Indigenous children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".