Proposals to Improve the Accuracy of Bilingual Public Signs LES PROPOSITIONS VISANT A AMELIORER LA PRECISION D'AFFICHAGE DES SIGNES BILINGUE EN PUBLIC
Bibliographic record
Abstract
Accurate translation of bilingual public signs not only provides foreign visitors necessary information in life but also is an essential symbol of internationalization of the city. However, because of the lack of competence of the translator, cultural differences and other factors, it is not an easy job to translate the public signs correctly. As a consequence, there are still many noticeable problems in public signs translation. This paper attempts to summarize some frequent errors found in sign translation and the possible causes of these mistranslations and comes up with some strategies for the proper translation for bilingual public signs. Key words : Public sign; Translation; Mistranslation; Strategies Resume Une traduction fidele d’affichage public bilingue fournit non seulement aux visiteurs etrangers les informations necessaires dans la vie mais c’est aussi un symbole essentiel de l’internationalisation de la ville. Toutefois, en raison du manque de competence du traducteur, les differences culturelles et d’autres facteurs, il n’est pas une tâche facile a traduire les signes publics correctement. En consequence, il y a encore de nombreux problemes notablement dans la traduction des signes public. Ce document tente de resumer quelques erreurs frequentes trouvees dans la traduction des signes et des causes possibles de ces erreurs de traduction et arrive avec quelques strategies pour la traduction correcte des signes bilingues publics. Mots cles : Signe public; Traduction; Contresens; Strategies
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".