Teaching Intercultural Communication in China and Australia: Intellectual and Contextual Constraints and Opportunities
Bibliographic record
Abstract
As the world turns more towards China through trade, tourism and knowledge exchange, Chinese professionals will increasingly need to communicate directly with foreigners inside China. This face-to-face communication will require not only linguistic and communicative competence, but also a deep cultural knowledge of China as well as of other cultures, to help strangers adapt effectively to Chinese cultural contexts and to improve mutual understanding. In this paper we suggest that it might be useful for Chinese teachers of intercultural communication to examine their assumptions and practices by comparing them with those in other countries. We illustrate this argument through a comparison of the teaching of intercultural communication in Yunnan with an equivalent program in professional education in Melbourne. We argue that there are many similarities in the two programs, reflecting their common disciplinary basis. There are also differences between the programs reflecting different assumptions about teaching and learning, and different contexts of intercultural communication. This comparison helps identify the cultural and contextual influences on what is currently identified as appropriate in Yunnan, and the possible constraints on how much the program could be altered without clashing with acceptable aims, strategies and outcomes. Key words: Intercultural Communication; Communicative Competence; Professional Education; Globalization
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".