Designing a Robust System for Skilling Young Workforce from India to Enhance Overseas Migration Outcomes
Bibliographic record
Abstract
According to a study by Korn Ferry, the world will likely face a labour deficit of 6% and 11% in 2025 and 2030, respectively. This study also reports that the unrealized output value due to the labour deficit will likely be $8.9 trillion in 2030. Many countries worldwide will face labour deficits due to an ageing population and a lack of skilled professionals. Contrary to this, India has the highest population at approximately 1.437 billion, 17.76% of the global population. According to the information published by the External Affairs Ministry Government of India, with the world’s largest working-age population, India will have a labour surplus of 245 million workers by 2030, half of the global need. A robust model of skilling in the Country of Origin (COO) with a surplus workforce focussing on the requirements of the Country of Destination (COD) with a deficit workforce can enhance migration outcomes of global workforce mobility to match demand and supply. The current migration process from India (Country of Origin) to the Country of Destination (COD) is complex and influenced by irregularities. The resultant cost of migration (for study or jobs overseas) through remittances of foreign currencies, both in the legal and illegal form (often due to the involvement of scrupulous agents), is very high. The major motivation of the study was to critically analyse the current process of migration and devise a robust system for skilling and overseas migration of young workforce from India to: a) Reduce the ‘Cost’ (C) of the skilling and migration process. b) Reduce the ‘Lead time’ (LT) of the skilling and migration process. c) Maximise the ‘Yield’ (Y) (% of trainees/skilled professionals getting jobs overseas). d) Minimise the ‘Risk’ (R) (not getting employed after migration, underemployment after migration, and fraud by unscrupulous agents, etc.). e) Minimise the “Illegal migration.” During the pandemic, we realised the importance of the role of the healthcare workforce in lifesaving and the need to create a global supply chain of skilled healthcare professionals. In this research, an in-depth analysis has been carried out to understand the demand and supply side requirements of the healthcare workforce at global level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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; both teacher heads agree on what is shown here.
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".