THE STUDY OF THE IMAGERY OF IDIOMS WITH SOMATISMS IN THE CANADIAN VERSION OF THE FRENCH LANGUAGE, STANDARD FRENCH AND THE ENGLISH LANGUAGE
Bibliographic record
Abstract
The article presents a comparative study of the imagery of the phraseological fund of the Canadian version of the French language, standard French and the English language. The purpose of the research is to describe the main ways of forming idioms with somatic components based on the material of the designated languages. The main objectives of the study are to identify specific, unique, as well as universal ways of reflecting the reality surrounding a person on the basis of figurative idioms with somatic components, to determine the main types of displacement and reconsideration of the meaning of a free combination of words that led to the creation of an idiom. The novelty of the work lies in the fact that for the first time it generalises the knowledge of somatisms’ imagery in the phraseological fund of the languages being compared. For correct understanding of idioms, it is necessary to trace the connection between the meaning of a free word combination and the way of its acquiring expressiveness, evaluative judgment through various means of creating an image. The anthropocentric nature of language as a whole suggests that a human being in general and parts of human body, in particular, are a measure and standard for describing and reflecting reality phenomena. The conclusion is made that somatisms in three languages under consideration are highly productive; most idioms resulted from metaphorical reconsideration, metaphorical transfer, or figurative comparison; idioms resulting from metaphorisation prevail in quantitative terms.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".