Types of Interlanguage Phraseological Correspondences (Based on English and Turkish Languages)
Bibliographic record
Abstract
Contrastive study of phraseology is important for general linguistic problematics, theory and practice of translation, interpreting lexicography and teaching foreign languages. We can state similarities and differences in phraseology of different languages based on current comparative studies. Many linguists use material of non-kindred languages in comparative studies. The objective of such researches is detection of both general features—universals common for any language and differential features typical for individual languages. The distinctive feature of phraseological units’ correlation is that phraseological units are more complex than their components both by structure and meaning, it also should be mentioned the infrequency of the “form-meaning” relation. The objective of the paper is distinguishing and studying two phraseological groups of English and Turkish languages associated with the notion of “family” that, to the best of our knowledge, weren’t studied before in each of languages separately and, particularly, in a comparative way. The study endeavors to correlate PU in both languages by basic parameters in order to define the universal and the differential, and also detect inter-linguistic equivalences and compensatory mechanisms in the area of difference. Results of comparative study of PU contribute to distinguishing general and specific features of their structural models, detecting facts of one language system influence on the other, and also detecting regularities in PU translation from one language into the other.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".