What to Keep, What to Let Go: The Case of Indians from Nyasaland
Bibliographic record
Abstract
This paper examines how identity may be constructed in the case of multiple transnational migrations within just a couple of generations, using an example (or examples) of immigrants from India to Nyasaland (present-day Malawi) to Great Britain to Canada. What happens to your sense of identity when your Indian ancestors emigrate to Nyasaland, you grow up there, but around 1964, soon after independence from Great Britain, all Indians, including you and your family, are deported from your homeland of Nyasaland? In one case study analyzed in this paper, a Nyasa Catholic woman of Indian descent first travels to Great Britain (as a British citizen) for college, marries a man of Indian descent from Nyasaland, and together they emigrate to Canada. How much Indian identity is retained in this scenario; and, how much of Nyasaland, particularly since the colonial African state no longer exists but is now independent Malawi? And further: How much identity is constructed around Canadian practices? What factors contribute to the development of national and cultural identity for twice or thrice migrants? How might they decide what to keep and what to let go?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".