COMPARISON OF THE POSSIBILITIES OF THE CONTEXTUAL METHOD USING IN THE TURKISH AND ENGLISH LEXICOLOGY
Bibliographic record
Abstract
The relevance of the English Language learning is substantiated in the article for many reasons. First, because of its prevalence in the whole world. Secondly, due to the huge number of lexical and stylistic features, such as context, polysemantic words, direct word order in sentence, variability (British, American, Canadian, Australian, New Zealand English). Thirdly, owing to its clarity, conciseness, emotional colouring and individuality. The article defines the possibilities of the contextual method using in the Turkish and English language Lexicology studying. Such teaching methods as descriptive (for a general description of the context); contextual-interpretive (to identify the functional and semantic meaning of a word), as well as a method of creating a problem situation using a contextual task were used for achieving the goal. The features of the English language as the language of international communication are determined; the place of the context in English is considered and the role of the English context in comparison with the Turkish one is defined. The difficulties of translating words from English and vice versa due to their ambiguity are stipulated. Especially it concerns synonymic dominants, idioms, set phrases and phrasal verbs. Context has been shown to understand the meaning of a word or phrase. Depending upon the context and lexical surroundings, most words in common vocabulary can change their meaning in both Turkish and English.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".