Assessment In Translation Studies: Research Needs
Bibliographic record
Abstract
On the whole, most research into assessment in translation only concentrates on one area — evaluation of translations of literary and sacred texts — and other areas are ignored. In fact, this field of research includes two other areas, each with its own characteristics: assessment of professionals at work and assessment of trainee translators.Starting with this presupposition, we describe the three areas and analyze the notion of translation assessment, so as to define the characteristics of each area: objects, types, functions, aims and means of assessment. Next, we discuss the question of translation competence, and the concepts of translation problems and translation errors, in order to reach a general principle that should be applied in all assessment. Finally, we suggest assessment instruments to be used in teaching translation and make suggestions for research in assessing translator training, an area that has long been neglected and deserves serious attention.
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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 | 0.443 | 0.521 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.022 | 0.060 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".