The Translation of Wordplay from the Perspective of Relevance Theory: Translating Sexual Puns in two Shakespearian Tragedies into Galician and Spanish
Bibliographic record
Abstract
The present paper aims to analyse the translation of puns from a relevance-theory perspective. According to such theoretical framework, the relation between a translation and its source text is considered to be based on interpretive resemblance, rather than on equivalence. The translator would try to seek optimal relevance, in such a way that he or she would use different strategies to try to recreate the cognitive effects intended by the source writer with the lowest possible processing effort on the part of the target addressee. The analysis carried out in this study is based on two tragedies by Shakespeare – namely,HamletandOthello– and on five Spanish and two Galician versions of those two plays. The strategies used by the translators of those versions to render sexual puns have been analysed, focusing not only on the product but also on the process. The selection of strategy is determined, among other factors, by the specific context and by the principle of relevance. In those cases in which there is a coincidence in the relation between the levels of signifier and signified across source and target language, translators normally opt to translate literally and reproduce a pun based on the same linguistic phenomenon as the source text pun and semantically equivalent to it. In the rest of the cases, the translator will have to assess what is more relevant, either content or the effect produced by the pun.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".