News Discourse in Translation: Topical Structure and News Content in the Analytical News Article
Bibliographic record
Abstract
Topical structure in news translation has received relatively little attention despite its stated significance in discourse content and in producing functionally adequate translations. Journalists write news stories with a given structure, order, viewpoint and values, which are “transferred” in translation and affect the way topics are organized. This study explores how shifts in topical development in translation influence rhetorical structure and ultimately news content. Using Lautamatti’s Topical Structure Analysis and Bell’s Event Structure Model, the paper describes the translation strategies applied in (re)producing the source text’s topical and event structures in the target language in a corpus of Hungarian–English news texts (the summary sections of analytical news articles). Results show that while translators generally preserve the sources’ structure in translation, in some cases (e.g. sequential topic progression) significant changes occur, altering the status of some information as well as the event structure, thus producing modified news contents. The paper also examines whether the claim that news translation is influenced by norms similar to those regulating news production more generally applies to this news genre, too. Findings suggest that due to the stereotypical features of this genre, the data only partially support this claim.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| 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".