Book Review: Review of Lucja Biel: Lost in the Eurofog: The Textual Fit of Translated Law. Frankfurt am Main: Peter Lang 2014. (=Studies in Language, Culture and Society, Volume 2).
Bibliographic record
Abstract
To enhance audience memory plays an important role for an advertisement or its translation to realize the function of promoting a product or service.Accordingly, exploring the textual strategies to increase the memorability of an advertisement will be productive for translators.Studies on audience memory in advertisement translation are rare and this paper provides a preliminary research on the textual strategies that are supposed to be able to enhance audience memory, based on the findings of psychological studies, especially empirical studies on human memory and human needs.Referring to such investigations, the researcher has summarized four principles to strengthen text receivers' memory, namely focus, specificity, personal reference, and creativity.With reference to these four principles, specific textual strategies have been summarized in this study which can be applied in advertisement translation, including the use of repetitions, the provision of details, the application of personal pronouns, and the design of rhetorical figures.The application of these strategies in Chinese-English advertisement translation is discussed via a case study to demonstrate how the strategies are handled in translation and provide reference for translators working in this field.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".