Multilingualism in Modern Kazakhstan: New Challenges
Bibliographic record
Abstract
The paper is devoted to the problem of multilingualism and multilingual education in modern Kazakhstan. Kazakhstan is a multiethnic and multi-religious state, where live more than 126 representatives of different ethnic groups. At the present stage in the development of Kazakhstani society, bilingualism is gradually turning into multilingualism. One of the most important strategic goals of the language policy of Kazakhstan is the necessity of speaking several languages: Kazakh, Russian and English. Kazakhstan is currently implementing overall modernization of the education system and embedding a multilingualism policy into the educational process as well. The experimental sites for multilingual education have been initiated in several Kazakhstani Universities and secondary schools. The young Kazakhstani generation brought up in independent Kazakhstan is involved in multilingual education process. By the year 2020, 100% of the population is expected to speak the Kazakh language, 95% Russian and 25% English. For the implementation of these goals a fundamentally new type of learning - e-learning - is being developed. Its test-project has been initiated within 44 institutions. In 2014 the Ministry of Education and Science of the Republic of Kazakhstan is increasing the number of such institutions by providing them with the Internet access at the speed of 4 to 10 Mbit per sec. The outcome of the research is based on the sociolinguistic data which has been conducted in all regions of the country, using the methods of sociolinguistic data collection.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".