Global health diasporas: Tamil migrant doctors in times of war, Covid-19 and economic depression in Sri Lanka
Bibliographic record
Abstract
Most of the research studies about the Tamil diaspora do not address health, but rather focus on political and economic issues. Due to the ongoing disparities of health in Sri Lanka caused by the war, the Covid-19 pandemic, and economic crisis, it seems to be crucial to look at whether the diaspora actually tries to improve the health situation in their communities of origin. I analyse the health-related activities of the global Tamil diaspora organisations in various periods of the conflict in Sri Lanka: during wartime (1983–2009), the postwar period (2009–2020) and the COVID-19 pandemic and economic crisis (since 2020). I do that in the context of the broader worldwide Tamil diaspora, particularly in five main Western destination countries: United Kingdom, Germany, Switzerland, Canada, and the United States. The study reveals that the Tamil health diaspora runs different transnational health projects depending on changing political circumstances in Sri Lanka.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".