Active Listening in MTI Interpreting Introductory Course for Chinese Students
Bibliographic record
Abstract
MTI students who are native Chinese, though already acquired a reasonable level of English, were not able to adequately interpret the English speech into Chinese at the beginning of the course, mostly probably because of their inadequate comprehension. In order to be able to develop interpreting skills, students should be able to acquire the ability of active listening. The purpose of this study is to try to figure out indicators of active listening at the beginning stage of the interpreting course for the teachers to refer to in the identification of this stage of students’ development and form teaching strategies accordingly. The study is based upon the observation and analysis of the corpus of the national finals of the 3rd CTPC Cup All China Interpreting Contest, whose contestants were at the stage of active listening, and yet to be further developed to be professionals. It was found out that there are six indicators showing the ability of active listening and four types of practices can be formed based upon the six indicators. The research limitation is that it is yet to be further studied on how long the teaching of active listening should last in the interpreting course. In the interpreting courses, it is possible for the teachers to refer to the six indicators and the four types of exercises to help the students get through this stage of development and lay a good foundation for their further development in interpreting.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".