"They all talk Okanagan and I know what they are saying." language nests in the early years : insights, challenges and promising practices.
Bibliographic record
Abstract
Indigenous early language learning programs for young children, commonly known as “language nests”, are well established in New Zealand and Hawai‘i. By contrast, in Canada there are few such programs and the concept is not commonly known in Indigenous communities. This study presents the experiences and insights of twenty-one fluent Elders, administrators, language teachers, early childhood educators and parents who have been involved in language nest programs in the start up years. These interviews were shaped by research questions on key issues, challenges and promising approaches. A thematic analysis was used to highlight dominant themes and to honour the words and ideas of the participants. The participants in this study described the benefits that young children and fluent Elders experience through their involvement in early language immersion programs. Research participants shared visions for nests, deeply held beliefs about the need to fully immerse young children in their language, as well as promising approaches. These insights give evidence that Language Nests support young children to understand, speak and sing in the language, and that participation in these programs has the added effect of enhancing the daily lives of involved fluent Elders. This research is presented in service to the reclamation of early learning, Indigenous languages, and intergenerational ways of knowing and being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".