Misuses of English Intonation for Chinese Students in Cross-Cultural Communication
Bibliographic record
Abstract
Increasingly cross-cultural communications bring about more English learners in China. With attitudinal and discourse functions, English intonation plays an important part in cross-cultural communication. However, students in China have insufficient awareness of the important role intonation plays. Some students fail to tell the real intentions conveyed by the intonation of the speaker. Others misuse English intonations and lead to misunderstandings. These will have negative effects on a successful cross-cultural communication. Based on the former researches, this paper focuses on analyzing the common types of intonation misuses and exploring their root causes. It points out that the negative transfer of Chinese is one of the root causes for indecent intonation, and then comes up with several suggestions on how to avoid indecent intonations. It argues that learners should firstly realize the difference between Chinese and English intonation, focus more on intonation learning with a sound motivation and cultivate good learning strategies so as to reduce the negative transfer from Chinese and avoid indecent intonation. As more people realized the importance of intonations, decent intonation will help them achieve more and more successful cross-cultural communication.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".