The Adequacy and Acceptability of Machine Translation in Translating the Islamic Texts
Bibliographic record
Abstract
Islamic translation is considered as a special distinguished sub-discipline of applied linguistics. It is one of the most important areas of translation because it carries the values and eternal message. Through the history, the first translation work was of religious books. This study attempts to evaluate the adequacy and acceptability of four machine translation (MT) systems (World lingo, Babylon translation, Google translate, Bing translator) in translating the Islamic texts. In addition, it aims to evaluate the Islamic translation outputs based on functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability, and fidelity). The findings indicted that Google Translate System is the most adequate and acceptable among the other three systems (World lingo, Babylon translation, Bing translator) in translating the Islamic texts. The findings also revealed that Google Translate is acceptable in producing Islamic translation outputs in regard to the following functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability and fidelity) due to Google Translate advancement.
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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 | 0.027 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".