Cultural Basis of Metaphors Translation: Case of Emotions in Persian and English
Bibliographic record
Abstract
Metaphorical expressions often involve culturally-specific concepts, embodying associations related to a particular cultural community. Metaphor translation poses the problems of switching between different cultural references, as well as conceptual and linguistic perspectives. Dealing with metaphors in translation, thus, is not simply a matter of identifying the linguistic correspondences in two languages under study, but of identifying correspondences between their conceptual systems corresponding to their different cultural models. The main purpose of this paper is to present the findings of a study that investigated emotive metaphoric conceptualizations and their dominant patterns in Persian and English. The emotions under study are metaphorical expressions of happiness and sadness which have been compiled from a literary source text and its two corresponding target texts. The Metaphor Identification Procedures (MIP), proposed by the Pragglejazz group (2007), and Lakoff and Johnson’s (1980) Conceptual Metaphor Theory (CMT) were adopted as the framework for analysis. Our findings revealed that there are many cultural similarities and differences between emotive metaphorical concepts in Persian and English.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".