Manner-of-speaking in a corpus-based translation study of narrative texts
Bibliographic record
Abstract
This contribution examines manner-of-speaking (e.g. murmur, mutter) in a German>Spanish parallel corpus of narrative texts. This research was prompted by the fact that this phenomenon had not yet been paid due attention in the “Thinking-for-Translating” framework, in comparison with the phenomenon of manner-of-motion (e.g. limp, run). The starting point of this paper is thus the widely confirmed fact that, in previous works focusing on the translation of manner-of-motion between languages belonging to different typologies, some alterations regarding manner have been identified (e.g. omission, addition), and that this is mainly due to typological differences between source and target language. More precisely, speakers of satellite-framed languages (including German) often encode manner, while users of verb-framed languages (including Spanish) usually devote more attention to the lexicalization of path, sometimes at the expense of manner. Thus the aim of this paper is twofold: first, to examine translators’ behaviour regarding manner-of-speaking in a satellite-framed language>verb-framed language translation scenario (German>Spanish), specifically focusing on the translation of reporting verbs in a corpus of narrative texts; second, to compare the resulting data with findings from previous comparable studies dealing with the communication and motion frames. In the translation scenario studied here, the results suggest that translator behaviour differs when dealing with these two frames: while manner-of-motion is often omitted in translations into Spanish (from German), manner-of-speaking is usually transferred, or even added.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".