Translating Genre of News Stories and the Correlated Grammar in Analysing Student Translation Errors
Bibliographic record
Abstract
In translation studies, genre and grammar have each flourished in their own right as a subject of study by a number of scholars. But research solely dedicated to the complementary relations between genre and grammar has been rare, particularly from the translation education perspective. Neither genre nor grammar can function properly without the other in a text because context (genre) and ‘wording’ (grammar) are inseparable. The aim of this paper is to examine the correlation between genre structure and grammar in the analysis of errors in student translations of news story texts. Drawing on Systemic Functional Linguistics (SFL), translations of two subtypes of news-reporting texts from English to Korean are analyzed. The main data include two source texts and their translations by nine Masters’students. The findings of this paper show that a large majority of translation mistakes arise from a lack of knowledge of genre structure and its interconnection with logical meaning (how clauses, sentences and paragraphs are combined). The research reported in this paper indicates that genre structure and grammar together constitute useful resources for teaching the translation of news-reporting texts, with more studies of genre structure in other subject fields desired.
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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 | 0.018 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".