Integrating Chinese Culture into English Teaching: A Case Study of Oxford English for Junior High Schools
Bibliographic record
Abstract
The Compulsory Education English Curriculum Standard (2011 edition) pointed out that cultural awareness is one of five major curriculum objectives of the English subject. Learners of foreign language not only can decode the cultural connotation of the target language, but also can proficiently express the contents in their mother language using a foreign language. Textbooks as the major source for students to gain culture-related knowledge matter much for the cultivation of culture awareness. This paper selected Oxford English for Junior High Schools (Jiangsu Edition) as the research subject, using literature research method, and quantitative analysis method to tackle the following questions:What Chinese culture is involved in the unit topics and contents of Oxford English for Junior High Schools (Jiangsu Edition)? Which cultural teaching methods can be applied to classroom teaching? How to apply cultural teaching methods to English teaching? Research findings show that the Chinese culture in this textbook has a high frequency of appearance and is rich in topics, which has a certain role in cultivating students’ cross-cultural identity and communication skills. However, there are inadequate complete passages about Chinese culture, and the presentation of Chinese culture is superficial. Thus, the author proposed three specific cultural teaching strategies: cultural introduction, cultural integration and cultural contrast, so as to better integrate Chinese culture into the Junior High Schools English class.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".