Sense-for-Sense Translation and the Dilemma of Comprehensibility in Translating Jordanian-Laden Proverbs: A Literary Perspective
Bibliographic record
Abstract
Arising from the fact that there is always a top priority in choosing the appropriate equivalence with texts that are not straightforwardly understood we argued that cultural approximation strategies such as functional equivalence or what Fredrich Schleiermacher termed “domesticized translation” can be the best choice in translating culturespecific items i.e., proverbs and proverbial expressions. In this paper, we investigated the translatability of a number of culturally-laden expressions, mainly prevailing in Jordan. We also suggested translations that, we believe, captured the intended messages of the origin. Refuting arguments that advocated the employment of word-for-word translation, we argued that sense-for-sense and/or domesticized translation can function more faithfully and naturally within texts loaded with cultural components provided that translators should prove fluent and competent in the TL culture. Our argument is highly based upon our strong sensation that the audience in the TL doesn’t want to experience hard times in decoding much foreignized terms but he or she wants to feel at ease by living and dealing with domestic experiences that reflect upon his/her culture. Key words : Equivalence; Word-for-word translation; Sense-forsense translation; Text-typology; Domesticing translation; Foreingized translation
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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 | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".