Précis-writing, Revision and Editing: Piloting the European Master in Translation
Bibliographic record
Abstract
The paper reports on and discusses the authors’ development of and experience with Précis-writing, revision and editing , a pilot module developed especially for the European Master in Translation (EMT). The background, aim and important characteristics of the EMT are briefly explained. Inspired by the IAMLADP report from 2001, the module development included an exploratory survey of the translation industry internationally and in Denmark, employing web-based questionnaires supplemented by a focus-group interview with translator-editors of the European Commission. Our findings generated knowledge about professional précis-writing, revision and editing, including relevant norms and concepts. It also provided useful input on perceived training needs in this respect within the translation profession. The module development also comprised selecting a suitable theoretical foundation and designing a manageable course structure. Students’ written evaluations of a course taught in the spring of 2005 are summarized, and the paper concludes with the authors’ recommendations for others involved in university-level translator training.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".