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Record W3104802581 · doi:10.1007/978-3-030-51237-8_9

Diaspora Policies, Consular Services and Social Protection for Indian Citizens Abroad

2020· book-chapter· en· W3104802581 on OpenAlexaboutno aff
Daniel Naujoks

Bibliographic record

VenueIMISCOE research series · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersEuropean Commission
KeywordsDiasporaPolitical scienceGovernment (linguistics)Economic growthPopulationEthnic groupSocial protectionDevelopment economicsGeographySociologyEconomicsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.921
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.382
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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