Researching, Planning, and the Implementation of Tałtan Language Nests: Sharing our Experiences
Bibliographic record
Abstract
Since 2012, our nation has been working to revitalize and reclaim our language, with an emphasis on the creation of new speakers. Tahltan Elders have spoken about the importance of our young children learning our language, so a focus has been on teaching babies and toddlers in language nests. A language nest is a home-like environment for infants to learn the language in an immersion setting and provides opportunities for all generations to be part of the revitalization of a language. One of the authors carried out research that focused on Tahltan community experiences of language revitalization. The investigation focused on language revitalization’s connection to health and healing and what needs to be done to revitalize our language. Following recommendations from that research, language nests have been one of the vital components our Language and Culture Program has focused on. The ways in which community members in Tahltan communities have planned and implemented language nests will be discussed, along withe sharing experiences and activities that are currently being carried out.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".