Different Approaches to the Objects of Phraseology in Linguistics
Bibliographic record
Abstract
The article deals with phraseology and its object. Phraseology is known to be one of the difficult, debatable and interesting parts of linguistics. It appeared in the middle of the 19th century as a science, and was firstly dealt widely with by the scholars of the post-Soviet country. The early researchers of phraseology were Russian scholars and linguists such as Abakumov, Reformatski, Arnold, Bulakhovski, Ojegov, Amosova, Vinogradov, etc. Though being mostly investigated by Russian specialist, phraseology has been the target of the research of the following Azerbaijan linguists—Seyidov, Shiraliyev, Bayramov, Rustamov, Huseynzade, & Veliyeva. The subject matter of phraseology was very interesting to most linguists, however, it was impossible to originate a single theory on phraseology. In this article we have touched upon the main terms used in phraseology, such as, set expression, idiom, set phrase, fixed word-groups, word-equivalent, phraseological unit, etc. These terms are defined differently by some scholars. Connotational and denotational meanings of phraseological units are described discussed here. Besides, three approaches to the study of phraseological units (semantic approach, functional approach, contextual approach) are discussed in details.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".