Legal Translation and Court Interpreting: Ethical Values, Quality, Competence Training
Bibliographic record
Abstract
The Finnish system for authorising translators to produce legally valid translations was reformed in 2008, from a test measuring language skills into an examination containing translation assignments. The examination consists of two translation assignments and a test on the examinees’ knowledge of the authorised translator’s professional practices. In the assessment of the translation products, a predefined, two-dimensional assessment system is used in which translations are marked for both content and language quality. In this paper, we discuss the Finnish assessment system and compare it with the assessment systems used on examinations by the American Translators’ Association (ATA), the Canadian Translators, Terminologists and Interpreters Council (CTTIC), and the translation quality evaluation models SAE J2450 and MQM-DQF used within the translation industry. Drawing on a previous analysis of the use of the assessment system in the Finnish examinations and a pilot survey among the assessors in the Finnish system, we propose a new, simplified assessment 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 | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".