Kina’muanej Knjanjiji’naq mut ntakotmnew tli’lnu’ltik (In the Foreign Language, Let us Teach our Children not to be Ashamed of Being Mi’kmaq)
Bibliographic record
Abstract
Colonialism has assimilated and suppressed Indigenous languages across Turtle Island ( North America). A resurgence of language is needed for First Nation learners and educators and this resurgence is required if Indigenous people are going to revitalize, recover and reclaim Indigenous languages. The existing actions occurring within Indigenous communities contributing to language resurgence include immersion schools. Eskasoni First Nation opened its doors in September 2015 to a full immersion school separate from the English speaking educational centers. This move follows the introduction of Mi'kmaq immersion over ten years earlier within the English speaking school in the community. The Mi’kmaw immersion school includes the Ta’n L’nuey Etl-mawlukwatmumk Mi’kmaw Curriculum Development Centre that assists educators in translating educational curriculum from the dominant English language to Mi’kmaq. In this paper, stories are shared about the Eskasoni immersion program’s actions towards language resurgence through a desire-based lens, based on rich narratives from three Mi’kmaw immersion educators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".