FEATURES OF LINGUISTIC’S TERMS IN THE BOOK OF Ş. BEKTÖRE NAMED “TATARCA SARF, NAHV” / Ş. BEKTÖRENİÑ «TATARCA SARF, NAHV»1 ADLI KİTABINDA QULLANILĞAN LİNGVİSTİK TERMİNLERNİÑ H��SUSİYETLERİ
Bibliographic record
Abstract
The Characteristics Of Linguistic Terms Used In Ş. Bektöre’s Book Of “Tatarca Sarf Nahv”The first quarter of previous century became an important era for Crimean Tatar language and literature. The amendments in language policies, such as displacing Arabic alphabet by Latin alphabet and later replacing it by Cyrillic alphabet, resulted in some problems. Among the problems, there were spelling, terminology, schools and preparing lecture books. About these issues, the scientists such as B. Çoban-zade, Ş. Bektore, Y.A. Bayburtlı, A. Odabaş prepared important studies. In this article, we focus on the work of Şevkiy Bektore,“Tatarca Sarf Nahv”, who is a poet, author and educationalist, and the characteristics of linguistic terms used in this work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".