A Book is a Home: Revisiting Space and Place in M.G Vassanji's A Delhi Obsession
Bibliographic record
Abstract
The definitions of migration and diaspora are somewhat different. On the one hand, migration, be it voluntary or involuntary, is a movement for settlement, whereas the term diaspora describes the spread of people from their homeland. Diaspora studies, however, evolve so much that most types, if not all, of migration, come under diaspora studies. M. G. Vassanji's novel A Delhi Obsession (2019) showcases the complex relationship of the protagonists with their ancestors’ land. This paper seeks to analyse the complex relationship of Munir with his ancestor's homeland, who, as a third-generation migrant, looks at India from a different lens and multiple perspectives. India emerges as a mystical space for Munir—a tapestry woven from stories, narratives, and memories, but it gradually transforms into a place of identity through his ancestors’ roots and his own experience. Munir's experiences in India help his hybrid identity develop into a more complex one, which is similar to Vassanji's identity. M.G. Vassanji, a writer of the diaspora, explores the intricate relationship he has with India, paralleling the experiences of his protagonist, Munir. This connection is shaped by Vassanji's ancestors’ journey from India to Kenya and his subsequent relocation to Canada. This paper explores how the act of reading, alongside Vassanji's own journey, transforms the text into a virtual space of home for both the author and the reader. Where the text becomes the alternative memory through which Vassanji, as well as the readers can visit or imagine the home, which has their roots.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| 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".