An Investigation on the Translation Competence of Undergraduates of English Translation Majors in the Context of Autonomous Learning
Bibliographic record
Abstract
With the acceleration of globalization and frequent international exchanges, the translation industry has gradually become an important career field. In this context, training translation professionals are also becoming increasingly important. This paper uses translation students from the Guizhou University of Finance and Economics as a sample to investigate and study the current situation of translation professional ability in the context of autonomous learning. The collation and analysis of the questionnaire and the summary of the test results show that the overall translation competence of undergraduate translation majors is not optimistic, mainly as follows: low bilingual ability, lack of vocabulary and incoherence; Insufficient ability to translate extralinguistic issues, especially cultural components; Lack of translation expertise and poor mastery; Single use of tools; Translation has no strategy and so on. Of course, there are students' reasons, such as learning attitudes, and external reasons, such as the setting of teaching courses. Based on the research results, this paper puts forward some suggestions on how to improve translation capabilities to provide references for subsequent research.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".