Treatment of Cultural Differences in Translation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With more and more frequent interaction between China and the West, translation plays an extremely important role in communication. Translation is no longer viewed as simple linguistic transference between two languages; cultural factors should be taken into consideration in translation process. This paper tries to analyze how the cultural differences should be dealt with in translation process. Three concrete methods are proposed to deal with different kinds of cultural factors: literal translation with cultural explanation, loan translation, and faithful translation. It is the translator’s responsibility to choose the best strategy to render cultural differences. This paper emphasizes that translation shoulders the responsibility of making the original culture intelligible to the target reader and enriching the target culture. Therefore, when a translator is confronted with cultural factors, he/she must try his/her best to overcome the untranslatability caused by the incomparability between two cultures by choosing proper translation strategies. Only through this way, the translator could play the medium role in disseminating culture.
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.
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 | 0.000 |
| 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 it