Bibliographic record
Abstract
For many years, foreingn language teaching in our country pays close attention to language itself, but ignores the importance of culture. Therefore, many miscommunications may appear in cross-culture communication. This thesis focuses on the relationship betweent culture and language and emphasizes on the importance of culture in languge teaching. Besides, it supplies some useful and concrete methods to bring culture into language teaching according to the writer’s own teaching experiences. Key words: language teaching, culture introduction, concrete methods Resume: Dans l’E/A des langues etrangeres de notre pays, il arrive souvent que la negligence de la connotation culturelle affecte l’effet de communication, et conduit meme parfois a l’echec de communication. Face a ce phenomene, l’article present expose l’importance de la culture dans l’E/A des langues, la relation entre la culture et l’E/A des langues etrangeres et des mesures concretes pour l’introduction de la culture dans l’E/A. Mots-cles: E/A des langues etrangeres, introduction de la culture, mesures concretes 摘要：在我國外語教學中，常常出現由於忽視文化內涵的輸入而影響了交際的效果、甚至導致許多場合交際失誤的案例。本文針對這一現象，闡釋了文化在語言教學中的重要性、文化與外語教學的關係以及文化引入教學的具體實施措施。期待能對外語教學提供新的思考。 關鍵詞：外語教學；文化引入；具體措施
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".