Research Report on the Publicity of Hangzhou’s International Image: The English Translation of Global Communication Language
Bibliographic record
Abstract
Through the two main methods of field investigation (data collection) and personal interviews, this research report, combined with studies of translation on global communication language in China, investigates the English translation of global communication service in Hangzhou by using professional OCR recognition software and computer-aided translation (CAT) technology, explores the confusion in the translation of these language collected from the West Lake scenic area, museums, the Asian Games’ main venues and traffic along the route, etc., then carrying out detailed classification and analysis of translation errors and analyzes their occurrence in many aspects. Finally, it puts forward corresponding countermeasures and suggestions to improve the translation quality. The report points out that global communication language is an indispensable part of China’s foreign language services, city’s international language environment and global image construction, and the translation is also related to China’s foreign language education planning and city-level policy research. Translation of global communication language should use appropriate translation strategies, and should be leveled up to foreign communication translation and cultural exchanges between China and foreign countries as well as be examined from the perspective of external publicity and construction of international city image. The Hangzhou government should also effectively insure the translation quality, service quality and city benefits of global communication language on a macro level.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".