Bilingual Signboards on Lombok: Approaches to Acquiring the Translation Equivalence
Bibliographic record
Abstract
It is particularly intriguing to analyze the application of Baker's theory of translation equivalence through research on such brief texts as on signboard because it reveals a lot of interesting patterns. In order to make a signboard read in the least amount of time feasible and to get the word across to the reader in a more expedient manner, short phrases are frequently used. Texts taken from Lombok's newly emerging market for multilingual signboard served as the basis for this research's collection of data. This descriptive qualitative research aims to investigate the compatibility and applicability of the extended levels of Baker’s translation equivalence in the context of the collected data found on bilingual signboards at religious tourism sites on Lombok Island, particularly in relation to ethical, moral, and semiotic considerations. The study reveals the complex nature of translation equivalence in signboard, highlighting different levels such as word-level, grammatical, text-level, pragmatic, semiotic, and ethical equivalence. It offers practical insights for signboard designers, translators, and the tourism industry worldwide, providing essential information to unfamiliar tourists, contributing to their navigation and exploration of destinations. Additionally, the study underscores the significant role of signboard in tourism development, effective communication of information, intercultural understanding, and the growth of tourism in various destinations beyond Lombok.
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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".