Bibliographic record
Abstract
Following an earlier study by the same author on professional translators which appeared in Target 2000 (12:1 127-149), this article reports on an empirical study, based on both quantitative and qualitative data, on the learning needs of translation students, another major stakeholder in translator training. This study shows that contrary to a widely held assumption, the great majority of students taking translation did not and do not intend to be professional translators/interpreters. It is found that translation students prized training of both L1 and L2 before or during translation training, and that they preferred practice-oriented courses to theoretical courses. Also revealed in this study is that many students believe the current translation program does not reflect the market needs very well and that measures such as offering more practical courses, strengthening language training, teachers’ providing more detailed comments on assignments, etc., must be taken in order to improve the program. Based on such findings, a comparison with the earlier study on professional translators is made and pedagogical implications are also drawn in relation to some of the focal issues in translator training.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".