Modele kształcenia tłumaczy w zakresie wiedzy specjalistycznej z dziedziny prawa
Bibliographic record
Abstract
The aim of this paper is to present and discuss the existing models of training future translators specialising in legal texts at the university level. The analysed programmes were selected from undergraduate teaching programmes included in the European Master’s in Translation (EMT) project. Due to specific solutions in the field of teaching legal translation, other programmes encompassing the specificity of legal translation are taken into account. As far as future translators specialising in legal translation are concerned, four components of teaching programmes seem to be important: the translator training curriculum, the significance of legal translation and other related subjects in the curriculum, and, finally, the profiles of the trainer and the student. It should be emphasised that each of these components plays an important role in the development of the programmes designed to educate professional translators. What is more, taking into consideration the above research criteria, four models of teaching can be distinguished that are based on the distinction between two basic components of teaching programmes, i.e., the legal part and the translation. All four models are described and analysed providing the account of the two abovementioned components as well as their relation to the overall translator training curriculum. The analysis leads to the conclusion that four models of education in the field of legal translation can be observed, i.e. the translation model, translation model with basic legal background, translation model with extended legal background and, finally, the legal model.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".