Perceptions about Cultural Loss in Translating Idioms from English into Persian: A Case Study on the “Death of a Salesman” (Miller, 1949)
Bibliographic record
Abstract
Idioms, based on the Pederson‟s (2005) view, were considered as culture-bound elements. Therefore, idioms‟ problems were discussed as a sort of cultural problems basedon the cultural losses‟ categorization of Hanada Al-Masri (2010). Regarding Newmark‟s (1988) view about the existing deficiency in translation and Nord‟s (1991) view about lack of a common translation code for all cultures, the aim of this research was to provide a platform to discuss the deficiencies of translating idioms in cultural context and to get the translator general theory in translating idioms by the means of Naturalness theory. The researcher applied qualitative-quantitative methods in conducting this research by means of direct observation of source language idioms and their Persian translation. Based on the findings, the researcher found that complete loss, the loss of symbolic value and figurativeness of idioms, was the most rated phenomenon and the explicit loss, the loss of a cultural counterpart, was the least rated one. Also it was found that the translator general theory was to deliver a natural translation and to turn unfamiliar idioms into familiar words and idioms in order to make the translation acceptable and understandable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".