Diaspora Policies, Consular Services and Social Protection for Indian Citizens Abroad
Bibliographic record
Abstract
Abstract As the country with the world’s largest emigrant population and a long history of international mobility, India has adopted a multi-faceted institutional and policy framework to govern migration and diaspora engagement. This chapter provides a broad overview of initiatives on social protection for Indians abroad, shedding light on specific policy designs to include and exclude different populations in India and abroad. In addition to programmes by the national government, the chapter discusses initiatives at the sub-national level. The chapter shows that India has established a set of policies for various diaspora populations that are largely separate from the rules and policies adopted for nationals at home. Diaspora engagement policies, and especially policies aimed at fostering social protection of Indians abroad, are generally not integrated into national social protection policies. There is a clear distinction between policies that are geared towards the engagement of ethnic Indian populations whose forefathers have left Indian shores many generations ago, Indian communities in OECD countries – mostly US, Canada, Europe and Australia – and migrant workers going on temporary assignments to countries in the Persian Gulf. The chapter offers a discussion of the key differences, drivers, and limitations of existing policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".