Bibliographic record
Abstract
语言技能，语言技能是语言运用能力的重要组成部分。语言技能包括听、说、读、写四个方面的技能以及这四种技能的综合运用能力。听和读是理解的技能，说和写是表达的技能；这四种技能在语言学习和交际中相辅相成、相互促进。学生应通过大量的专项和综合性语言实践活动，形成综合语言运用能力，为真实语言交际打基础。因此，听、说、读、写既是学习的内容，又是学习的手段。在本课时的教学中，充分利用情境激活课堂，语言输出阶段也需要恰当的情境，这样才能使学生真正会用所学语言。文化意识，语言有丰富的文化内涵。在英语教学中，文化主要指英语国家的历史、地理、风土人情、传统习俗、生活方式、文学艺术、行为规范和价值观念等。接触和了解英语国家的文化有利于对英语的理解和使用，有利于加深对本国文化的理解与认识，有利于培养世界意识，有利于形成跨文化交际能力。要扩大学生接触异国文化的范围，帮助学生拓展视野，使他们提高对中外文化异同的敏感性和鉴别能力，为发展他们的跨文化交际能力打下良好的基础。
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".