An Examination of the Errors Committed by Iranian MA Students in their Translation of Advertisement Slogans Based on Keshavarz’s Taxonomy of Errors
Bibliographic record
Abstract
The present work is an attempt to reveal the nature of translating advertisement slogans over two different cultures by investigating the errors that will occur by Iranian MA translation students in translating a corpus of advertisements from English into Persian and vice versa. The sample of this study consisted of sixty MA students of translation studies who were randomly selected from three Azad university branches (Fars Science and Research branch, Tehran Science and Research and Bandar Abbas Azad University). The instruments used for data collection included a questionnaire consisted of eight advertising slogans in English and seven in Persian for products that were internationally marketed. The statistical procedure to analyze the data was Chi-square procedure to illustrate the frequency and percentage of errors occurrence. The results demonstrated that the ratio of wrong answers in the two languages is not the same. Generally, participants committed more errors in the translation of the Persian advertisement slogans comparing to English ones. Most errors participants committed in Persian translation slogans were related to grammatical and lexical interferences and most errors participants committed in English translation slogans were related to misunderstanding and misinterpretation of semantic and pragmatic aspects of the slogans.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".