Training the Trainers: Towards a Description of Translator Trainer Competence and Training Needs Analysis
Bibliographic record
Abstract
There is now a relative wealth of Translation Studies literature on translator training, but it often centres on impersonal aspects such as processes, content or activities, and ignores the human factor. There are two sets of participants in the teaching and learning process, both of whom are essential for its success: students or trainees, and teachers or trainers. Other than to bemoan their supposed deficiencies, or to design elaborate entrance filters, little has been said about students. But even less has been said about trainers. In this paper, attention focuses on them. The little that TS literature says about trainer profiles is mostly centred on the need for them to have professional translator competence. This paper takes a broader approach to the issues surrounding translator trainers and their training, setting them firmly within the broader context of higher education teaching as a profession, and attempts to link recently developed professional standards in higher education teaching to our field. This background allows the author to draw up a competence-based profile of the translator trainer and briefly to review which areas of such a profile have been addressed in TS and which are still in need of further work. The paper ends with an overview of the preliminary results of a study currently underway in Spain, designed to carry out detailed training needs analysis for translator trainers.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".