The Coronavirus Pandemic and the Overseas Indian Migrant Workers’ Crisis: Impacts on Polity and Foreign Policy
Bibliographic record
Abstract
The COVID-19 pandemic has brought the existential crises of Indian migrant workers, both domestic and overseas, to the centre stage of public discourse like never before.Millions of Indian migrant workers are stranded in foreign countries after losing their jobs; they face an uncertain future and even starvation.The migrant workers' plight is fast turning into a major humanitarian crisis for which the current union government was not prepared.This paper seeks to analyse the various facets of the migrant workers' crisis, the importance of remittances in the Indian economy, the rising cases of coronavirus infection among overseas Indians and the challenges of repatriation, mitigation efforts by the Indian government and implications of the migrant workers' crisis for the Indian economy, society, politics and foreign policy.Finally, an attempt has been made to recommend policy measures to protect the interests of Indian migrant workers.According to a Quartz India report, India has as many as 20 million migrant workers in different parts of the world.About half of them are in the six Gulf countries of Qatar, Bahrain, Oman, United Arab Emirates (UAE), Kuwait and Saudi Arabia.Among them, 2.5 million have migrated from Kerala (Pullanoor 2020).It must be noted here that migrant workers based in the Gulf region were not the only ones returning to India amid the COVID-19 crisis.Thousands of workers in the United States of America (USA), the United Kingdom (UK), Singapore, Australia and Canada returned to India as well during the pandemic. 1 The percentage of Indian migrant workers present in the major destination countries is shown below:
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".