A Corpus-Based Study on Original English Abstracts and Translated English Abstracts: A Case Study of Passive Voice and Pronouns
Bibliographic record
Abstract
On the basis of a large amount of corpus-based studies on translation works, the translation universals hypothesis is proposed. As it claims, translations enjoy some general features and Baker (1993) summarizes them into three universals, namely simplification, explicitation, and normalization, which are supported by many following researches. However, some of the later studies contradict with these rules in several ways, and the usages of passive voice and pronouns are the two most controversial issues. Previous researches suggest that according to the universal features of explicitation and normalization, translated texts tend to have a lower frequency of pronouns while over-representing the passive voice. To examine such claimings, 160 original English abstracts from two leading journals in the field of translation studies, The Translator and Translation Studies, and another 160 English abstracts from Chinese Translator Journal and Chinese Science & Technology Translators Journal, which are translated from Chinese abstracts, are collected. Two corpora are then constructed, namely the Original English Abstracts Corpus (OEAC) and Translated English Abstracts Corpus (TEAC). The CLAWS Part-of-speech Tagger is used to tag the lexical items and word processing tool AntConc 3.2.4 is used for retrieving the words. The comparison between the two corpora suggests that the translated English abstracts contain a lower level of frequency in the use of both passive voice and pronouns, which partially query the hypothesis of explicitation and normalization. A detailed analysis shows a higher frequency of past-tense passives in the OEAC and more passives in perfect tense in the TEAC. The OEAC also contains more relative pronouns while the other contains more indefinite pronouns. The norm theory is utilized to account for such phenomena. The detailed results of the study are expected to shed some lights on professional translating and academic writing.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".