Corpus Methodologies in Literary Translation Studies: An Analysis of Speech Verbs in Four Spanish Translations of Hard Times
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it