Current State and Difficulties in Chinese MTI Teachers’ Professional Development
Bibliographic record
Abstract
Teacher development plays a pivotal role in the program of Master of Translation and Interpretation (MTI) but there was inadequate research on their current state and needs in China. This research aimed to investigate their current state in teaching, academic research and translation practice and present a detailed description of their age, professional title and academic qualifications. A mixed methodology design was adopted to ensure reliability and validity of data by means of triangulation. The sample in this study was 514 teachers from 32 provinces, municipalities and autonomous regions in China. They were surveyed via quantitative questionnaires and online interviews. The findings were as follows.1：Despite reasonable age structure, around 50% of them had less than three years’ teaching practice in MTI. 2: There was an increase in the figure for MTI teachers with doctoral degree but they felt it difficult to offer learners professional guidance. 3: A large percentage of MTI teachers were in a comparatively slow stream of promotion in the title of professional post. 4: The percentage of the academic achievements associated with translation in all the research projects and papers was small. 5: A majority of these university teachers worked as part-time translators. 6: About half of them were not very content with the effectiveness in their staff training program. Therefore, it is recommended that more chances be offered for the MTI teachers to conduct translation practice in the professional sector and more researches be needed to meticulously understand their requirement in terms of career development with a view to bridging the gap between the resources in the teacher training and their actual needs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".