An Empirical Study on the Improvement of Students’ Strategic Competence Through Translation Project Teaching
Bibliographic record
Abstract
Strategic competence, as a meta-cognitive ability, determines the other translation sub-competences. To tease out how students’ strategic competence is developed in translation project is significant in enlightening the translation teaching practice. This study explores how five Chinese college students’ translation competence, particularly their strategic competence, develop within a translation project introduced into the translation teaching of the authors’ institute. Strategic competence includes four parts: problem identification, solution proposal, action taking, and decision making. By examining both the translation process and the translation product, including translation tasks, translation logs, group discussions, and interviews from five student translators, we found that the overall translation competence of the student translators has significantly improved. The development of various elements of strategic ability is uneven, with the abilities to identify problems, evaluate issues, and take measures showing the most significant improvement. However, there is a lag in decision-making capabilities in translation output. Based on these findings, the study provides concrete suggestions for improving the teaching of translation.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".