The Adequacy and Acceptability of Machine Translation in Translating the Islamic Texts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p><strong></strong>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it