Passive Voice and the Language of Translation: A Comparable Corpus-Based Study of Modern Greek Popular Science Articles
Bibliographic record
Abstract
Little research has been conducted so far into the translation-specific features that are dependent on both the source and the target language. This study aims at examining whether Modern Greek translated popular science articles differ from non-translated ones by being closer to the source language, which is English, in terms of the frequency and the word order of the passive voice constructions. This is one of the few Modern Greek studies that use a comparable corpus in order to better understand the nature of the translation practice. The corpus analysed consists of Modern Greek popular science articles and is divided into two subcorpora: the translated language corpus and the non-translated language corpus. The study indicates that there is substantial evidence that Modern Greek articles employ some translation-specific features which are dependent on the source language, at least in terms of some passive voice features. More importantly, it suggests that the non-translated texts tend to be similar to the translated ones, which are in turn closer to the English source texts. Even though it is early to conclude that translation encourages the different usage of particular linguistic features in non-translated texts, the data provide indirect evidence that translation is a potential field of language contact with important consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".