Translation and Technical Communication: Chicken or Egg?
Bibliographic record
Abstract
Translation starts with a document in one language and ends with a document with the same meaning in another language. Technical communication entails designing and writing a document from scratch in one language. The answer to the question of “Which, of translation or writing, comes first?” seems relatively obvious – the document needs to be written before it can be translated. However, when looking at translation and technical communication as professions and examining how the professionals are trained, the answer is not quite as clear-cut. In the United States, translators and technical communicators have different qualifications, different skills – in particular different language skills – and have degrees in different fields. Only recently has there appeared a certain convergence between the professions. In Europe, and more specifically in France, the profession of technical communicator is quite recent, as are the corresponding academic programs. Many technical communicators came to the profession from translation. The convergence therefore is perceived as being far greater. The purpose of this paper is to launch a comparative study of the competences or skills of translators and technical communicators, based on the existing European Master’s in Translation (EMT) list of competences for translators. The goal of this study would be to define the core skills for technical communicators, to examine to what extent they overlap with the competences of translators and ultimately, to establish a referential for training programs in technical communication.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".