Two translations of Mahfouz’s _Awlad Haratina_ (Children of our Alley): A computational-stylistic analysis
Bibliographic record
Abstract
Comparative studies of different translations for the same source text can be valuable sources of insights relevant to the fluid notion of ‘translation style’. Such studies can employ a wide variety of techniques, including computational analysis which targets specific elements in the text in order to allow for a systematic view of translator style. This study attempts a computational-stylistic analysis of the two English translations of Naguib Mahfouz’s controversial novel _Awlad Haratina_ (literally, Children of our Alley). The aim of the study is two-fold. First, it aims to show how quantifiable computational and distant reading techniques can help identify patterns of stylistic differences between these two translations. Second, it attempts to situate the results of this analysis within the wider social context of the two English translations (Stewart 1981 and Theroux 1996) of one of the most famous modern Arabic novels. The results clearly show patterns of linguistic use specific to each of the two translations highlighting differences in lexical variety and richness, sentence structure, readability level, stylometric analysis as well some lexical choices. These results can be interpreted within the social context of producing those two translations, with particular reference to characteristics of retranslation as discussed in the literature.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".