Sustaining Indigenous languages and cultures: Māori medium education in Aotearoa New Zealand and Aboriginal Head Start in Canada
Bibliographic record
Abstract
ABSTRACT In this paper, we examine stakeholder initiatives to revitalise Indigenous languages in two countries, Aotearoa New Zealand and Canada, the countries in which we live and conduct research. We provide a brief overview of the history of systematic Indigenous language and cultural suppression within our two countries, situating Māori Medium Education in Aotearoa New Zealand and Aboriginal Head Start in Canada; initiatives designed to revitalise and sustain Indigenous languages and cultures through the education of children within their generally parallel historical, social and political contexts. We draw on semi‐structured interviews and focus group conversations to highlight perspectives of Māori family members and students in Māori Medium Education and of Anishnaabek early childhood educators in northern Ontario Aboriginal Head Start programs. Participants indicate that these programs are making a difference in revitalising and sustaining Indigenous languages and cultures. Our comparison of positive outcomes and challenges that need to be addressed, based on stakeholders participating in initiatives in two countries, can inform broader conversations about Indigenous language revitalisation through initiatives focusing on early childhood education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| 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".