Adjectival Phraseological Units with the Parametric Component
Bibliographic record
Abstract
The article is devoted to the comparative study of the Tatar and English adjectival phraseological units with the parametric component. Structural and semantic peculiarities are taken into consideration. The main methods of research are those of general linguistic methods and special linguistic methods. The purpose of the article is to find out common and specific features of the adjectival phraseological units with the parametric components in the English and Tatar languages. The investigation is based on the material from English and Tatar monolingual and polylingual dictionaries. The adjectival phraseological units of the Tatar language have not been studied enough. The Tatar and the English languages are structurally different and the study of the adjectival phraseological units with the parametric component is of great interest. Comparative and non-comparative adjectival phraseological units with parametric components have been analyzed in the article, the frequency of the structures in both of the languages was found out and the explanation of the phenomenon was searched for. Semantically the adjectival phraseological units with the parametric component may express negative or positive attitude to reality, spheres of life, communicative process, or to a person, and that depends on the usage of this or that parametric adjective of the antonymic pair. General and specific features have been found out as well as exceptions. Based on the data got from the research the conclusions on the structure and semantics of the adjectival phraseological units with the parametric component are presented in the article.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".