Emphatic Italics in English Translations: Stylistic Failure or Motivated Stylistic Resources?
Bibliographic record
Abstract
This article argues that emphatic italics, a typographic feature regularly ignored by linguists and associated with poor style, have an important stylistic function in English, often working in implicit association with prosodic patterns in spoken language to signal marked information focus, thus fulfilling an important role in information structure and adding a conversational and involved tone to written texts. Emphatic italics are more common in English than in other languages because tonic prominence is the preferred means of marking information focus in English, while other languages use purely linguistic devices, such as word order. Thus arises the question of what happens in English translations from and into other languages. The study presented here looks at results obtained from a bidirectional English-Portuguese corpus (COMPARA) which suggest that italics may be less common in English translations from Portuguese than in non-translated English texts. This trend could potentially be explained by the use of common features of translated language, in particular explicitation and conservatism (also known as normalization). However, a closer look at the work of particular translators shows that the avoidance or use of italics is not a consistent feature of translations and may be a characteristic feature of the stylistic profile of certain translators.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".