Return to Home: In Michael Ondaatje’s Running in the Family and Romesh Gunesekera’s the Sandglass
Bibliographic record
Abstract
Migration involves departure from one’s native home, and also returning back. The returning migrants bring back unique stories to share with their people, and these stories of dislocation unveil ambivalent experiences due to multiple identities, rootlessness, cultural dilemmas, in-between condition, and loss of native mooring. Migrants leave their homes for upward mobility, personal venture, or due to some unbearable situations at home which forces them to move out. Despite getting assimilated into a comfortable life in the new nations and attaining new identities, there is a sentimental yearning in them, and they cannot be in their original selves. In diaspora there is a constant debate about homelessness, and the agony of being located and then dislocated. Migrant writers are keen to trace their roots and write about the social as well as political happenings of their native homelands. The two stalwarts of Sri Lankan diaspora writing, Michael Ondaatje and Romesh Gunesekera, seek to portray displaced immigrants living in the countries of the author’s adopted homes, Canada, and England. This is clearly illustrated in their respective novels Running in the Family and The Sandglass. Their books project displaced characters who straddle between two homes, two cultures and two identities, creating an expatriate identity that lacks fixity of roots due to living in two worlds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".