Cohesive Devices in Translator Training: A Study Based on a Romanian Translational Learner Corpus
Bibliographic record
Abstract
Formal links are naturally associated with cohesion as one of the main features of discourse. Cohesion has been extensively discussed in the literature especially in terms of the mechanisms generating it, but also in terms of its equivalence in translation. As with any type of discourse, the communicative value of translated texts is enhanced by their cohesive texture. Less attention has been granted to the translation of formal links carrying additional functions though. This study examines some cohesive devices in student translations with a special focus on the translatability of ellipsis, substitution and reference when they are enriched with stylistic, sociolectal and rhetorical values. The study is based on a translational learner corpus consisting of Romanian graduate students’ translations of a short story from English into Romanian. The methodology for assessing and analyzing the learner corpus is of both quantitative and qualitative nature and employs simplification, explicitation and neutralization. The conclusions comprise insights into some problematic areas in the trainees’ translations, as well as observations related to contrastive aspects of cohesive devices between English and Romanian. A teaching methodology is subsequently derived from the findings in an attempt to offer a more comprehensive approach to the pedagogy of translating cohesive devices with stylistic load.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".