<i>In-Between World</i>and<i>Worlds Within</i>: Reading Diasporic Return in Vassanji and Bissoondath
Bibliographic record
Abstract
The return journey has long been recognized as a central feature of diaspora, yet contemporary diaspora studies have conventionally understood it in stubbornly mythological terms. This paper looks to foster discussion about physical diasporic return journeys by juxtaposing M. G. Vassanji’sThe In-Between World of Vikram Lall(2003) and Neil Bissoondath’sThe Worlds Within Her(1998). Although the two novels overlap in their exploration of the Indian diaspora against the backdrop of racially volatile independence movements in former British colonies, they offer starkly different renderings of diasporic return that are reflected in their engagements with diasporic history. The paper closes with a consideration of the critical assumptions that have allowed the return journey to be overlooked in diaspora literary studies to date, suggesting that its absence may reflect the methodological nationalism of the field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".