The Skopos Theory and Tourist Material Translation: With an Analysis of Mt. Lushan Translation
Bibliographic record
Abstract
With Chinese opening and reform policy, more and more foreigners come to China for sightseeing. With the rapid growth of Chinese tourism industry, a further study in translation of English for tourism is a must, since, now, there exist some problems in translation of English for tourism. This thesis, guided by the Skopostheorie of translation and with the author’s analysis of two English versions of Mt. Lushan, is a tentative endeavor to find some way to solve the existing problems in translation of English for tourism. Key words: The Skopos theory; Tourist material translation; Translation of Mt. LushanResume: Avec l'ouverture chinoise et la reforme de la politique , il y a plus en plus des etrangers qui viennent voyager en Chine. Avec la croissance rapide du secteur touristique de la Chine, une nouvelle etude dans la traduction d'anglais pour le tourisme devienne une necessite , car , maintenant, il existe des problemes de la traduction d'anglais pour le tourisme. Cette these, guidee par la traduction de Skopostheorie et avec l'analyse de l'auteur de deux versions anglaises de Mt. Lushan, essaient de trouver des facons afin de resoudre des problemes existants de la traduction d'anglais pour le tourisme.Mots-cles: La theorie Skopos; Traduction materielle (substantielle) touristique; Traduction de Mt. Lushan
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".