Is early immersion effective for Aboriginal language acquisition? A case study from an Anishinaabemowin kindergarten
Bibliographic record
Abstract
Indigenous people in North America and around the world are in dire circumstances with respect to language maintenance and cultural continuation. However, Indigenous communities are also taking back increasing control of the education of their children. In so doing, they are frequently exploring culture-based education and language immersion models as a means of perpetuating language by passing it on to the youngest generation. This is the goal of the Mnidoo Mnising Anishinabek Kinoomaage Gaming (MMAK) Anishinaabemowin (Ojibwe) immersion school on Manitoulin Island. In this paper, we describe the linguistic results of early years education at the MMAK. We begin with a description of the development of the MMAK and share its successes and challenges in the framework of larger policy developments in the region. We then discuss the linguistic outcomes thus far for students in the MMAK; having collected data with Junior and Senior Kindergarten students over the past two years using multiple assessment methods, we have been able to establish clear patterns with respect to the impact of Anishinaabemowin language immersion on the language development of these students. Finally, we explore how Aboriginal language immersion may be a tool for language revitalization for this and other communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".