Thinking Path Schema of English Translation for Chinese Classics: An Empirical Study on Translation Schema in Translation Courses
Bibliographic record
Abstract
In view of the complicated translation cognitive process, the study investigated and compared students’ translation process of Chinese Classics through translation thinking path schema. Seventy-six participants learning translation courses based on parallel level in two classes of one Chinese university, some of them trained for four months intentionally, were involved into some translation experiments with selected ancient Chinese classic poems. By the form of discussions, cooperation, or individual written translation, data were gathered from manuscripts, answer sheets, video recordings, think-aloud questionnaires, reflection papers, and interviews, which were integrated and categorized into the process classification evaluation tables in qualitative and quantitative analysis for the empirical study. Through some visible comparing and contrasted data elucidation, results indicated the obvious advantages of making use of the thinking path schema in Chinese Classics translation among trained students, who have presented more diversified translation thinking courses and superior evaluation scores in general. What’s more, author could be regarded as an element considered into the angle of translator, but not an independent angle as other non-Chinese-classic-text translation process, amending the former thinking path schema. Furthermore, the conclusions and amendments after translation experiments could be considered into the dynamic translation process for Chinese classics.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".