National Consciousness in Intercultural Communication Textbooks: Taking Intercultural Communication: A Comparison of Chinese and English Cultures as an Example
Bibliographic record
Abstract
The Implementation Plan for the Construction of National Planning Teaching Materials for Undergraduate Courses in General Higher Education in the 14th Five-Year Plan formulated by the Ministry of Education in 2023 emphasizes the importance of national consciousness in textbooks. Under this background, this study draws on the analytical framework of national cognition and national identity proposed by Zhao and Shen. Through content analysis, this study analyzes the national consciousness in the textbook: Intercultural Communication: A comparison of Chinese and English Cultures, with the aim to deeply understand the role of national consciousness. The study finds the following characteristics: reasonable proportion of traditional Chinese culture, diversified Chinese cultural elements, large proportion of English translation of names observing the Chinese traditions, and abundant English expressions with Chinese characteristics about traditional Chinese culture. However, there are also some deficiencies, including the uneven distribution of cultural elements, insufficient content about modern Chinese policies and a lack of common culture. The researcher offers the following suggestions: increasing the number of Chinese cultural figures and cultural products, improving the comprehensiveness of culture, increasing the English expressions with Chinese characteristics related to modern Chinese policies, and enhancing the common culture.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".