Metaphorical Models of Ophthalmology Terms in Kazakh and English
Bibliographic record
Abstract
The article is devoted to the identification of cognitive mechanisms of metaphor in term coining. The introduction provides an overview of the metaphor study. Scientific works on metaphor in foreign and domestic linguistics are analyzed. The relevance of the research is determined. As the basic hypothesis of the research, it is possible to say the verbal expression of the pragmatic reworked special scientific knowledge, which shows the language of metaphors in Kazakh and English terminology of ophthalmology, the basics of mental activity, professional experience, and linguistic and cultural competence of specialists. The method of component analysis, modeling method, and etymological and statistical methods were used to determine the structural and semantic characteristics of metaphorical units. A syntactic analysis of Kazakh and English metaphor terms of ophthalmology was made, as a result of which two-component metaphor terms prevailed in both languages. Metaphor-terms created by the semantic method are divided into several frames based on the similarity of 1) colour, 2) shape, 3) colour and shape, 4) construction, 5) other features, 6) action, 7) service, and a conclusion was drawn with specific examples. The study of cognitive mechanisms of metaphors in the termsystem of ophthalmology showed that they have universal characteristics and national-cultural features. Natural and anthropomorphic metaphorical models in Kazakh and English termsystems of ophthalmology were determined and proved with concrete examples. It was concluded that the completion of the term system by metaphor-terms is realized as a result of the professional thinking of the terminologist or specialist.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".