English Translation of Linguistic Signs: A Study from the Perspective of Linguistic Landscape
Bibliographic record
Abstract
To bring convenience to the life and work of the foreigners who live and/or stay in China but do not know Chinese, a large number of linguistic signs have been set up in bilingual forms at almost every corner of cities, and even towns. Those correctly and properly-translated signs are helpful, practical and informative, hence enhancing the image of the cities. However, current situations of the Chinese-English translation (C-E translation) of linguistic signs are far from being satisfactory. Some poorly or even mistakenly-translated signs often cause confuses to foreigners and may even mislead them. To some extent, it will damage cities’ images as well as China’s international image as a whole. Therefore, the author chooses to conduct a tentative research on this topic from the perspective of linguistic landscape, in the hope that it would inspire further research and provide useful references for the research and practice of C-E translation of linguistic signs in future practice.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".