Diversity Sells: Uzma Jalaluddin’s Muslim Adaptation of<i>Pride and Prejudice</i>
Bibliographic record
Abstract
Pride and Prejudice (1813) is transposed onto an Indian-origin Muslim community in modern-day Toronto in Uzma Jamaluddin’s Ayesha at Last (2019), and the novel is as much about being Muslim in the West as it is about being an Austen adaptation. These creative departures from the Austen hypotext contribute to the novel’s positive reception, which can be gauged from the 4.4 stars rating by 1184 users on Amazon. Ronald Robertson (1995) argues that “diversity sells,” and this article examines Amazon user reviews to demonstrate how Jalaluddin’s Muslim glocalization of Pride and Prejudice makes her novel a success and reveals the market for such diverse stories. She makes a commendable effort to make space for practicing Muslim protagonists in the Austen oeuvre and succeeds in providing realistic depictions of many aspects of the Muslim community. However, the novel’s unfortunate surrender to Western stereotypes of the “terrorist” Muslim male to appeal to the implicit white reader ultimately undermines its authenticity and does not fully represent the breadth of Muslim experience, thereby demonstrating that continued effort is required to overhaul the publishing industry’s employee and audience base to enable the inclusion of more equitably drawn minority characters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".