Bibliographic record
Abstract
Abstract Scholarship in modern Arabic literary studies has treated the literature of the Lebanese Civil War, particularly novels written by women, in some depth. One of the most important texts used in both scholarship and teaching about this war is Ḥanān al-Shaykh’s Ḥikāyat Zahrah, translated as The Story of Zahra. This article focuses specifically on the one chapter in the novel narrated from the point of view of the protagonist’s uncle in order to explore how the English translation dramatically changes a number of elements in the original text. It uses insights from translation studies to show how significant changes to the novel in translation produce a text that serves particular ideological functions in English, consistent with a horizon of expectations that constructs Arab women as oppressed and passive victims of war. The article analyzes specific translation choices—most notably the extensive editing out of words, sentences, and passages—to demonstrate how the character of Zahrah’s uncle is changed in English and depicted as an unsavory and abusive man with little background, context, or history that would help the reader to better understand the character’s actions and motivations. It also shows how cutting out elements of the uncle’s story serves to depoliticize the text in English, divesting it of its local political context and changing its meaning and function as a novel about the Lebanese Civil War. The article is grounded in postcolonial, feminist translation studies, especially those dealing with Arabic fiction, to argue that the English-language novel The Story of Zahra functions within an ideological field that recycles stereotypes and tropes about Arab women. It will propose that the translation changes here depict Arab men against Arab women, rather than in relation to them, and subordinate the analysis of politics and communal relations to a more individual and individualized story of one exceptional woman.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".