Lost in Translation: Shop Signs in Jordan
Bibliographic record
Abstract
Shop signs, in the Jordanian public commercial environment, have invariably been studied from linguistic, sociolinguistic, and pragmatic perspectives, but they have been utterly ignored from a translational point of view . This study, the first of its kind, investigates various problems and inadequacies pertinent to the subject under discussion. Shop signs are selected here from a number of heterogeneous cities, and the translation errors therein, committed by communicators, were empirically analyzed and categorized. Language and culture are, of necessity, inextricably intertwined, and this nexus is particularly apparent in the world of local commercial shop signs, and thus it has been tackled for its direct relevance to the translation of these signs. This investigation, therefore, highlights the linguistic (e.g., word-order, wrong lexical choice, and reductionist strategies), and extralinguistic (i.e., sociocultural and promotional) factors that have turned out to lead to translation inappropriateness and unparallelisms, information skewing , and, consequently, serious semantic-conceptual problems in the produced TLTs. This study may, in a way, provide educated insight into the trendiest translation practices in this field, and the way shop signs are most often verbalized, mishandled, and mistranslated .
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".