Shifting Paradigms of Globalization: The Twenty‐first Century Transition Towards Generics in Skilled Migration from India
Bibliographic record
Abstract
Globalization of human capital through international migration is no longer about global physical presence only; it is also about global applicability of skills across various fields of specialization. This marks the main characteristics of skilled migration from India to developed countries in the twenty‐first century. The focus is shifting away from professionals in specific occupations, like doctors, engineers, scientists, architects, bankers, to information technology (IT) professionals embodying, in a way, more generic skills. In other words, it is the generic applicability of information and communications technology (ICT) which has led to large‐scale migration of Indians skilled in IT. Moreover, the exodus comprises not only the fully trained and educated workers going abroad for employment, but also students ‐ the semi‐finished human capital ‐ pursuing higher education in onshore as well as offshore universities of the developed countries. The new emigration is directed towards traditional host countries in the West such as the UK, Canada, and the US, but also towards newly emerging destinations in continental Europe (Germany, France, Belgium, Italy, Denmark), Australasia (Australia, New Zealand), East Asia (Japan, Republic of Korea), and South‐East Asia (Singapore, Malaysia). By using mainly current information and informal data as reported in the media, this article perceives emerging trends and changes in the context of the global labour market for skills, and suggests a possible framework towards evolving strategies of remedial development.
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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 | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".