The Translation of Hadiths on Prophet Muhammad’s Personal Attributes: A Foreignising Approach
Bibliographic record
Abstract
As a category of religious texts, prophetic hadiths present distinct challenges for translators. This study aims to investigate the linguistic and cultural issues inherent in Najjar’s (2012) translation of hadiths concerning prophetic personal attributes. The analysis encompasses a selection of eight hadiths, four of which illustrate linguistic challenges, while the remaining four exemplify cultural difficulties. The theoretical framework employed in this study is a foreignizing approach, which seeks to preserve the linguistic features, cultural norms, and value systems of the original texts in their English translations. To facilitate this process, Baker’s taxonomy (1992) is utilized to identify and evaluate samples with linguistic issues, whereas Newmark’s cultural categorisation (1988) is applied to select and assess samples with cultural concerns. Specifically, linguistic samples are chosen based on Baker’s taxonomy, which analyses equivalence at both the word and grammatical levels, while cultural samples are selected in accordance with Newmark’s concept of ‘material culture,’ encompassing aspects such as food and clothing. Additionally, Halimah’s (ACNCS) criteria (2015) are employed to analyse and evaluate the quality of the translated hadiths. The findings of this study suggest a pressing need for the retranslation of prophetic personal attributes hadiths through the application of a foreignising approach. Although the scope of this research is not exhaustive, it is anticipated that the study will contribute to the understanding of these texts among non-Arab Muslims and stimulate further scholarly inquiry in this area.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".