The function of recurrent word-combinations in English translations from three different languages
Bibliographic record
Abstract
This article compares phraseological tendencies in translated vs. non-translated English through functionally classified 3-word sequences. The study builds on previous research that compared 3-grams in fiction texts originally written in English with fiction texts translated from Norwegian. The current investigation adds English translations from two additional languages – German and Swedish – with the aim of establishing to what extent the tendencies noted for English translations from Norwegian extend to English translations from other languages. Thus the study contributes to the discussion of translation universals and translation as a third code. At the level of 3-gram functions, it has been uncovered that English originals and translations share similar functional characteristics in eight of the fourteen categories identified. Of the remaining six, four show statistically significant differences between originals and translations, regardless of source language. Based on a more qualitative study of four specific 3-grams from two of these categories, it is concluded, in line with the previous studies, that the most likely explanations are source language(s) shining through and the (potentially universal) tendency for translators to use a smaller and more fixed set of expressions in their translations.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".