Explicitation, Unique Items and the Translation of English Passives in Thai Legal Texts
Bibliographic record
Abstract
Despite growing interest in the study of translation in Thailand, relatively little has been published on contemporary Thai translation practices. This study presents a first attempt to explore legal translation into Thai, using both a purpose-built parallel corpus of international treaties translated from English into Thai, and a monolingual corpus of non-translated Thai legislation. Drawing on previous corpus-based studies concerned with general features of translation, we ask whether there is evidence of explicitation (Becher 2010a) in our translated data and whether “unique items” (Tirkkonen-Condit 2002) are underrepresented. We pursue these questions through a study of the translation of the passive voice into Thai, and focus in particular on the Thai passive markers ถูก /thùuk/, โดน /doon/, and ได้รับ /dai-rup/, which we consider unique items in Thai. The study finds that, despite some writers’ antipathy to the passive voice in Thai, most English passives are translated into Thai using the passive voice, and that in those instances where the active voice is used in the translation, there is rarely any explicitation involved, as explicit agents are rarely added in Thai. We do not find any evidence to support the hypothesis that unique items are underrepresented in translation. On the contrary, the unique items studied appear to be overrepresented in translation into Thai.
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 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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".