Identity and Islamophobia in twenty-first century British Muslim novels
Bibliographic record
Abstract
This thesis examines the ways in which contemporary British Muslim novelists are exploring Muslim identity and depicting Islam in a context of heightened Islamophobia. The authors have been chosen for the ways in which they are complicating and re-shaping established literary forms and genre expectations, including the bildungsroman and ‘family marriage plot’, and reworking established issues and themes, including racism in Britain, family and friendship. The works have been selected for their engagement with British Muslim identity as represented and dramatised in the media and popular culture, and for how they intervene in contemporary debates about state multiculturalism and secular liberalism, not least how the role of religion is shaped differently in different diasporic contexts and how anti-Asian racism is fuelled by Islamophobia. In order of discussion, the primary texts are: Nadeem Aslam’s novel Maps for Lost Lovers (2004); Leila Aboulela’s The Kindness of Enemies (2015) and Robin Yassin-Kassab’s The Road from Damascus (2008); Samir Rahim’s Asghar and Zahra (2019) and Monica Ali’s Love Marriage (2022); Tariq Mehmood’s Song of Gulzarina (2016) and Nadim Safdar’s Akram’s War (2016); and The Study Circle by Haroun Khan (2018). My readings encompass how Muslim novelists are positioned in Britain by publishers and reviewers, the ‘burden of representation’ British writers who are Muslim are expected to carry, and the pervasive (neo- )orientalist discourse that continues to shape how Muslim characters are read, or risk being read. I consider how contemporary ‘framings’ of Muslims in Britain are filtered through government programmes like the Prevent strategy, and how the novelists I have foregrounded are mounting a critique to address as well as explore the deleterious effects of surveillance on Muslim communities, as well as delimiting representation and characterisation. The thesis explores how these contemporary British novelists are building complexity into representation of Britons who are often maligned.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".