India’s International Migrant Workers: Geopolitics and Beyond
Bibliographic record
Abstract
The challenges faced by immigrant workers are pivotal in today’s global discourse. The recent 2024 United States election and the upcoming 45th Canadian elections reflect these sentiments, while developing countries express concerns regarding restricting foreign workers and increasing uncertainty in the immigration process. This paper explores the various issues of wage disparities, legal barriers, exploitation, and social discrimination, as well as conflicts and wars faced by Indian international migrants. Through thematic context analysis, this paper aims to analyse these challenges. Key findings include the legal hurdles Indian workers/ professionals face, such as decades-long waits for green cards in the US, and new immigration reforms in Canada, affecting Indian students. Additionally, the paper highlights the intensifying xenophobia and anti-immigrant sentiments during the COVID-19 pandemic, worsening social discrimination against Indian migrants. The research concludes that addressing these challenges necessitates comprehensive reforms, and international organisations such as the International Labour Organisation (ILO), together with other UN bodies and the World Trade Organisation (WTO), must play active roles in bringing these concerns to light in the case of India. Furthermore, India’s forthcoming bilateral agreements should include a chapter on the movement of natural persons. Policy suggestions include implementing fair wage practices to close the pay gap between migrants and nationals, dismantling the kafala system in the Gulf Cooperation Council (GCC) to protect the rights of migrant workers, streamlining immigration pathways for skilled professionals and combating xenophobia through public awareness campaigns.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".