A Public Signs Translation Assessment to Binzhou City of China From the Perspective of Dynamic Equivalence
Bibliographic record
Abstract
Public signs are mainly used to convey certain information, attract the attention of tourists, and provide convenience for tourists. English translations of public signs in Binzhou City (mainly in Weji Ancient Village) reflect the cultural connotation and the level of internationalization of Binzhou city. However, the investigation report shows that there are quite a few problems in English translations of public signs in Binzhou, such as the lack of translation, redundant translation, improperly expressed and ambiguous words, and the lack of aesthetic feeling, which will mislead foreign visitors. English translations of the public signs in Binzhou should adopt correct translation principles, strategies and methods to improve the quality of English translations of public signs to help Binzhou city establish a fine city image and achieve the purpose of enhancing the competitiveness of Binzhou city. This paper takes Eugene Nida’s “dynamic equivalence” translation theory as the principle and collects public signs in Binzhou city as the research object, aiming to find out the existing problems of English translations of public signs in tourist attractions in Binzhou city. The paper is based on the principles of “formal equivalence”, “meaning equivalence” and “style equivalence” in Eugene Nida’s dynamic equivalence theory and proposes corresponding solutions so as to improve the quality of English translations of public signs and provide references for tourist attractions, which will improve internationalization of Binzhou city and make reference to the international image of China.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".