A Case Study on the Impact of Mother-Tongue Negative Transfer on Chinese-English Interpretation
Bibliographic record
Abstract
Interpreting is not only a simple conversion of language, but also a cross-cultural activity which aims to resolve language barriers and cultural contradictions in communication. How to avoid the interference of mother tongue becomes a focus of interpreting studies. This paper is a case study on the impact of negative transfer of mother-tongue on C-E (Chinese to English) interpreting. Twenty graduates from interpreting majors of Sichuan University were chosen to take a test of C-E interpreting full of Chinese features. Test results show that there are different degrees of negative transfer of mother-tongue in C-E interpreting, which have some negative impact on cross-culture communication. By studying the different types of negative transfer in the test results, some suggestions are given on how to reduce the impact of the negative transfer of mother-tongue from a broader view. We have every reason to believe that as more and more people consciously avoid the negative transfer of mother tongue in their C-E interpreting practices, they will greatly improve their interpreting quality and achieve 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".