Corpus Methodologies in Literary Translation Studies: An Analysis of Speech Verbs in Four Spanish Translations of Hard Times
Bibliographic record
Abstract
In this article, speech verbs in Dickens’s Hard Times (1854) and their translation into Spanish are analyzed. Apart from their basic function of introducing speech, these verbs can also contribute to characterization. The regular occurrence of a particular speech verb to report the direct speech of a particular character helps to create a fictional personality. Given the important role they may play, the rendering of such verbs in four Spanish versions of this novel is assessed. To do so, a corpus-based methodology has been employed. A concordancing software was used to retrieve speech verbs from the original novel, allowing their close analysis in context. Then, using an aligned parallel corpus containing the four versions, a comparison was carried out to see how they have been rendered. Evidence is provided that none of the four translations entirely preserves the characterizing value of the verbs, which may affect the way readers form impressions of characters in their minds. The use of this corpus metholodogy is thus seen to contribute to the field of literary translation studies.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".