Bibliographic record
Abstract
The subject of this research is the main initiatives undertaken in Nunavut aimed at preservation and development of the Inuktitut, which is the language of one of the three indigenous peoples of Canada – the Inuit. According to UNESCO, the Inuktitut alongside the majority of languages of the indigenous peoples of Canada, is currently classified as “definitely endangered”, and its preservation requires new solutions of the government. Indigenous languages are not only a means of communication, but also represent a complex system of the centuries-old knowledge, play a significant role in the identity of indigenous peoples, conservation of their culture, worldview, and self-determination. The problem of preservation of language as the crucial element of ethno-cultural heritage becomes increasingly important in the modern era of globalization, which has captured all countries and continents. The novelty of this study consists in analysis and assessment of the experience of the Canadian region Nunavut in implementation of bilingual education models in the regional educational institutions, as well as in the use of information technologies for popularizing the Inuktitut language. Despite the fact that the overall language situation in Nunavut remains complicate, immersion into the language on the levels of preschool and school education, development of media, television and radio broadcasting, the last two decades mark positive trends in the number of Intuits with the command of Inuit language. The experience of Nunavut is of practical importance for the development and implementation of federal and regional programs aimed at supporting the languages and cultures of the indigenous small-numbered peoples of Russia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".