Exploring the Influence that Different Ways of Thought have on Language Learning: Taking the Russian Language and Chinese Language for Example
Bibliographic record
Abstract
This paper begins from exploring such differences in ways of thought of the Russian people and the Chinese people as between integer-first and part-first, helical and linear thought, different sequence of time and space, form-oriented and meaning-oriented thought, precision-first and ambiguity-first and subjectivity-focus and objectivity-focus way of thought, then analyzes the influence the above differences have on the ways of thought of the Russian people and the Chinese people as well as on their languages. And finally explores the influence that different ways of thought between these two languages have on Russian Learning Key words: way of thought, difference, influence, the Russian language and Chinese language Resume: L’article present etudie l’influence des differences de facon de penser des Russes et des Chinois sur leur langue respective et leur expression, et sur l’apprentissage du russe, sous les six angles suivants : ensemble en priorite et partie en priorite ; spirale et lineaire ; ordre temporel et ordre spatial, cohesion par forme et cohesion par sens ; exact et vague, sujet et objet. Mots-cles: facon de penser, difference, influence, le russe et le chinois 摘要：本文從整體優先與局部優先思維方式差異；螺旋性與直線性思維方式差異；時空順序思維方式差異；形意合思維方式差異；精確與模糊思維方式差異；主客體思維方式差異等六個方面探討了俄漢民族思維方式的差異對俄漢語言及其表達方式的影響，以及俄漢思維差異對俄語學習的影響。 關鍵詞：思維方式；差異；影響；俄漢語言
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".