Between the Homeland and Diaspora: Identity Dilemma in Indian Literature
Bibliographic record
Abstract
The article aims to analytically and comparatively examine how the negotiation of identity is represented in Indian literature, both in the homeland and abroad, in a situation where cultures meet, collide, and merge. The focus of the article will be on four books: Aravind Adiga’s The White Tiger (2008), Bapsi Sidwa’s Water (2006), Kiran Desai’s The Inheritance of Loss (2006), and Bharathi Mukherjee’s Jasmine (1989). All the authors write about identity from a particular cultural context: the Bengali community in the USA, Brahmins in the Indian caste system, the privileged and less-privileged status of Indian individuals, and the hegemonic aspirations of the middle class that have taken the form of politics and produce socio-cultural inequalities. Because of the complex social structure dominated by the Indian caste system, one aspect of migrant experiences is the limitations placed by society on their identity, which can also be a critical determinant of their economic well-being and thus affect identity formation. Accordingly, the presumption is that the utility of the protagonists, both the immigrants and the locals in their homeland, encompasses economic well-being and cultural identity. Despite their sense of alienation, displacement, and rootlessness, the protagonists in all the novels that I have mentioned above manage to carve out a space of belonging for themselves, be it in their homeland or abroad, despite social, political, and cultural obstacles.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.048 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".