Translation Skills and Knowledge – Preliminary Findings of a Survey of Translators and Revisers Working at Inter-governmental Organizations
Bibliographic record
Abstract
Translators deploy a range of skills and draw on different types of knowledge in the exercise of their profession, but are some skills and knowledge types more important than others? What is the ideal combination nowadays? This study aims to investigate the relative importance of the different skills and knowledge that translators need in the specific context of translation at inter-governmental organizations. A survey was conducted of over 300 in-house translators and revisers working at over 20 inter-governmental organizations and with 24 different languages among them. The survey consisted of two questionnaires: one on the importance of different skills and knowledge, the other on the extent to which skills and knowledge are found lacking among new recruits. The results confirm that translators need more than language skills: in addition to general knowledge and in some instances specialized knowledge, they need analytical, research, technological, interpersonal and time-management skills. Correlating the findings of the two questionnaires produces a weighted list of skills and knowledge that can be used as a yardstick for adjusting training programmes and recruitment testing procedures in line with empirically identified priorities. The methodology should also be applicable to the identification of skill sets in other professions and contexts in which new recruits are closely observed, such as in-house interpreting.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".