NABİ'NİN HAYRİYYE ADLI ESERİNDEKİ DEYİM VE ATASÖZLERİNİN TÜRKÇE DERS KİTAPLARINDA YER ALMA DURUMLARININ İNCELENMESİ
Bibliographic record
Abstract
ABSTRACT The idioms and proverbs among the cultural elements of Turkic are important language products. It has been used extensively in every stage of the development of the Turkic in order to make the expression effective. It is also possible to encounter these items frequently in the Divan literature which covers a phase of six syllables. In this literature, many currents and schools have been formed. Hikemi poetry from these; Thought-based, guiding and teaching poetry. It is seen that frequently used idioms and proverbs are used in order to get a deep sense in the works given in this movement. In this study, Nihbi's Hayriyye, the forerunner of Hikemi poetry that lived in the second half of the 17th century and the first quarter of the 18th century, was handled in terms of these language products. In order to determine the items mentioned in the study, Hayriyye-Yusuf Nâbi prepared by Iskender Pala was used as a basis and the screening was carried out taking into account the frequency and number of usage of the idioms and proverbs in the 1647 head. In the survey; 270 vocabulary 221 different idioms, 8 vocabulary 8 proverbs were detected. 71 of the idiomatic phrases are used in their original form today in couplets. Expressions in Hayriyye were classified as compound verb and noun group in terms of their structural features. It was seen that the expressions in the form of compound verb group in the direction of the obtained findings were used more than the phrases in the name group form. The phrases in Hayriyye 5-8. Class Turkish textbooks were presented and tabled.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".