English-Arabic Translation of COVID-19 Prevention and Control Terminology: A Domesticating Approach
Bibliographic record
Abstract
The outbreak of COVID-19 in 2020 brought a crucial need for clear instructions to control and prevent the virus’s spread. In the context of the Arabic language, the demand for medical translators soared and the public needed clear health guidance more than ever before. This study aims to investigate the challenges of the English-Arabic translation of COVID-19 prevention and control terminology using a domesticating approach (Venuti, 1995) to overcome any challenges. A set of criteria, “conciseness, precision and appropriateness” (Giaber and Sharkas, 2021) is used for the assessment of the quality of the translation. Additionally, a questionnaire of English-Arabic translation samples is answered by 32 participants (26 males and 6 females), to evaluate the quality of these translations based on “clarity and naturalness” (Halimah, 2015). The results indicate that linguistic and cultural challenges are found in the English-Arabic translation of COVID-19 prevention and control terminology. They also indicate that the application of a domesticating approach improves their quality and helps to overcome linguistic and cultural challenges in translation.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".