SCIENTIFIC AND THEORETICAL FOUNDATIONS OF THE METHODOLOGY OF TEACHING THE UZBEK LANGUAGE
Bibliographic record
Abstract
The Uzbek language is one of the ancient languages and belongs to the Turkic language group of the Altai language family. At the present, the number of Uzbek speakers worldwide exceeds 40 million people. These are primarily residents of Uzbekistan, CIS countries, as well as Europe and the USA, Canada and many other countries who can speak this language fluently and considers Uzbek as their native language. With the growing interest in the history, culture, art, Uzbek literature, national customs and traditions of the Uzbek language, the influence of the Uzbek language in the world is also growing. At the same time, there is a high need for intensive and effective methods of studying the Uzbek language, as well as for mod-ern textbooks and teaching aids. However, there are still many problems in this area. In particular, the teaching of the Uzbek language according to international standards, based on a system of language proficiency levels, has not yet been established. The article tells about the history, develop-ment and current state of teaching the Uzbek language to foreigners. The experience gained over many years in the process of teaching the Uzbek language, the peculiarities of teaching the language as a native and foreign language are analyzed.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".