Indigenous Language Revitalization: Role of a Bilingual Speech-Language Pathologist
Bibliographic record
Abstract
Due to the risk of language extinction, immersion education is being implemented by Aboriginal communities in Canada and the United States as a language revitalization strategy. This paper describes one successful initiative, the Biidaaban Kinoomaagegamik Immersion Program (BKIP), started in 2006 by the community of Sagamok Anishnawbek (population: 1,400) situated on the north shore of Lake Huron in northern Ontario. The students are educated in Anishnaabemowin (Ojibwe), the primary language of instruction, through the day from senior kindergarten (SK) to Grade 3 with one hour allotted daily to English-language study. Aboriginal speech-language pathologists (SLPs), as speakers of an Indigenous language, can play a critical role in immersion education and language preservation. The role of the bilingual SLP in immersion programs is multifaceted. The varied aspects and responsibilities of this role are discussed.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".