Assessment procedures in translation degree programmes in Spain: Results of the EACT project
Bibliographic record
Abstract
The purpose of this article is to present the results obtained in a study of summative assessment in practical translation modules in translation degree programs in Spain and of the problems that exist in relation to assessment. Part of the EACT project, the study was conducted as a survey, using a specifically designed questionnaire. Its sample comprised 97 translator trainers who, between them, taught a total of 223 practical written translation modules. This article presents the profile of those trainers, the modules they habitually taught and the assessment tasks they used for grading in each module. Among the most common of those tasks are translations of texts, translation projects, students’ commentaries or reports on their own translations, translation error analysis and the analysis of the linguistic, textual and pragmatic characteristics of source texts. The article also identifies some of the main problems and challenges related to assessment in translation teaching, which notably include the subjectivity inherent in any type of assessment, the lack of standardisation of assessment procedures and the heterogeneity of translation students. It concludes by outlining the contribution of the EACT project, a key aspect of which is the design of translation-level tests.
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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 | 0.046 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".