Strategies Used for Translating Explicit and Implicit Meanings in Shakespeare’s Hamlet Into Arabic: A Relevance-Theoretic Approach
Bibliographic record
Abstract
With the use of the main assumptions of Relevance Theory (RT), the current research delved into three different Arabic versions of Shakespeare’s play Hamlet (named as Translations A, J and M) with regard to their methods used in treating explicit and implicit meanings. Firstly, concerning the explicit meanings, it was found that such meanings abound with transitional clauses. Although the three translations are, to some extent similar, they are also slightly different. Translation A attempts to use the structures and words with clear import for the hearers, not sticking to one-to-one correspondence. The other two translations (J and M) attempt to preserve the same structure. Secondly, concerning (the) implicit meanings, the study indicated that such meanings are a characteristic property of Hamlet. They render this play very difficult to deal with in terms of translation. By and large, it was found that Translation A and Translation J make use of the RT strategy Weakening the existing assumptions and combining with existing assumptions to generate the needed contextual implications as a tool to render Hamlet into Arabic, whereas Translation M uses the strategy of ‘Eliminating existing assumptions’ to render Hamlet into Arabic. In additions, the study argues that Translation A and Translation J are more faithful to the original text, since they keep mentioning all implicit meanings without omitting any, whilst Translation M is less faithful. Finally, the study found that RT strategy ‘Weakening the existing assumptions’ is mostly adopted according to its important role in keeping the translated text faithful without much loss of meanings and interpretation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".