Teaching Specialised Translation Through Corpus Linguistics: Translation Quality Assessment and Methodology Evaluation and Enhancement by Experimental Approach
Bibliographic record
Abstract
In the current context of rapid and constant evolution of global communication and specialised discourses, the need for devising methods for ensuring both high quality levels of specialised translation and successful translation training is becoming a true challenge. Steady renewal in knowledge paradigms leads to an increase in term coinage, modifications in lexical and phraseological patterns, and accommodations in discourse conventions. This situation requires teachers in specialised translation to train future translators to develop the skills meant to help them adapt rapidly to change. The tools brought by corpus linguistics offer access to the language-in-the-making and continuously emerging knowledge fields. However, methods for their efficient exploitation in translation classes can still be improved. In the current study, we present the translation-teaching framework devised specifically for such contexts. It is based on corpus linguistics, terminology management, collaboration with experts, and the quantitative analysis of the quality of finished translations, which can then, in turn, be used to improve the overall framework and to provide research material on specialised translation problems.
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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 | 0.137 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".