Encountering The ‘Other’: Diasporic Consciousness in Jasmine and Brick Lane
Bibliographic record
Abstract
Bharati Mukherjee and Monica Ali are both diasporic writers, from India and Bangladesh, respectively. Although Mukherjee’s growing up years were spent in India, it was her experience an immigrant in Canada, where she spent almost fourteen years of her life from 1966 to 1980, which provided her with the themes of her novels. The racism she encountered in Canada forced her to focus on issues such as cultural conflict, alienation, and gender discrimination, even gender violence. Her novel Jasmine encapsulates the experience of an Indian female immigrant to the US who despite various odds and hurdles, is able to survive and prevail. Monica Ali, a Dhaka born British writer, takes up gender problems as well as the issues of migrant community of Bangladesh and was hailed as the best of ‘young British novelists’ in 2003 for her debut novel Brick Lane. The novel explores the life of Nazneen, an immigrant in London, who becomes an embodiment of cultural conflict between east and west. The paper aims to bring out the fact that both women protagonists, Nazneen and Jasmine, as immigrants, adapt and survive due to the status of being the ‘other,’ which has been accorded to them since birth. Gender discrimination, which is a part of their life, turns them into fighters and survivors. The ‘otherness’ of their status, helps them acclimatise, while highlighting the commonality of their experience in terms of both, as females and immigrants.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.037 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".