Ethno-linguistic peculiarities of French Canadian and English Canadian linguistic world-images in comparative aspect
Bibliographic record
Abstract
In this article, the ethno-linguistic features of French Canadian and English Canadian linguistic world-images are subjected to comparative analysis. As the result of the mentioned linguistic world-images comparison according to a number of criteria, the author comes to conclusion that there is a significant number of differences between them. First of all, these differences come from the peculiarities of English and French Canadians’ historical paths: for a long time English Canadians and English have dominated in all spheres of public life, whereas francophone minority has been oppressed (which is reflected in idioms), and the use of French was confined to a family circle. The differences in morphological and grammatical features of languages determine the differences in mentality: though the system of tenses (respectively, the mental division of the time space by the nations) is quite similar, a greater analyticity of the English language and a greater linguistic flexibility of French is observed. When English Canadian and French Canadian phraseology is compared, the greater role of religion in the French Canadian community is evident, rather than in English Canadian; the influence of the Canadian variant of the English language on the Canadian variant of French is clearly expressed. With all the differences, both LWI share a number of common (common Canadian) concepts (northness, homeland, etc.) and values (tolerance, peacefulness, discretion, etc.). Key words : linguistic world image, concept, value, phraseology, ethno-linguistic specific feature.
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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 | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".