The Rôle of Theory in Translator Training: Some Observations about Syllabus Design
Bibliographic record
Abstract
“With doubts about the usefulness of translation theory never far from many people's minds, this paper seeks to consider exactly what it is that we are trying to achieve by including a theoretical component in translator training programmes. Within this context the paper specifically examines the possibilities of generic theory courses — in which students who are working with different language pairs and who probably have only a single language in common are all taught together — as opposed to a more language-specific approach. In order to attain the relevance that they purportedly so often lack, such courses need to set a fairly broad agenda for themselves, seeking if possible to address the type of questions likely to be uppermost in students' minds, expose students to a range of differing opinions on controversial issues, provide an alternative to standard dichotomies, encourage participants to arrive at their own strategies for solving translation problems, prepare students for work within the translation industry and demonstrate that translation is not an activity which is completely ad hoc and subjective. The paper furthermore suggests that every effort should be made to harmonise the formal theory component with everything else that goes on in the programme, so that theory is seen to be relevant to practice. Within this broader perspective one of the main purposes of this training component should therefore be to enable students to develop their own personal, internalised theory which will inform their developing performance as professional translators.”
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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 | 0.098 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".