Traumatized Voices in Contemporary Arab-British Women Fiction: A Critical Stylistics Approach
Bibliographic record
Abstract
Despite the interest often shown by feminist-informed models of literary trauma in the linguistic properties of traumatized characters’ language, very little has been done in relation to the study of the linguistic mechanisms/strategies speakers adopt in narrating traumatic events. This article explores the linguistic and discursive mechanisms in feminist trauma narratives, with a particular focus on the trauma of exile in the diasporic writings of Arab-British women novelists. Given the interdisciplinary nature of the topic, critical stylistics is adopted to describe the hidden discursive mechanisms in the speech of trauma victims, and how these mechanisms affect both the way such unsettling experiences are narrated, and the extent to which the traumatic dimension of these stories is properly conveyed to readers and recipients. Trauma theorists (Caruth 1995, Rogers 2006) have often emphasized the ‘unspeakable’ nature of traumatic experiences – the way in which they exceed the boundaries of language and expression. Accordingly, our attention should not be directed to what these texts explicitly say. Rather, we should be alert to their silences, gaps, and breaks. In other words, we should be more concerned with how language operates, because it is the cracks and crevices in victims’ speech that the full impact of trauma is most likely to be discernible. In the case of Arab-British women writers, traumatic memories of home, the anxiety of exile, and the constant search for identity are all negotiated through language. By adopting such linguistic strategies as repetition and negation, the traumatized characters in the selected texts both point to their in-between subject position, and assert their alternative subjectivity as resistant to clear-cut compartmentalization.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".