Training the Trainers: Towards a Description of Translator Trainer Competence and Training Needs Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it