Strengthening Skills and Education for Innovation
Bibliographic record
Abstract
More than half of India’s population—over 500 million people—are younger than 25. By 2050 India is expected to overtake China as the world’s most populous nation, and over the next five years will be responsible for nearly a quarter of the increase in the world’s working-age population. Already India has almost a third of the available labor supply in low-cost countries (NASSCOM and McKinsey 2005). These figures, pointing to India’s “demographic dividend, ” represent an enormous competitive advantage for India in its emergence as an innovation econ-omy, and as a potential world-class supplier of skills to the world. However, the widespread perception that India has unlimited employable human resources has changed. India has a growing shortage of skilled workers—caused largely by work-force development and education systems that do not respond adequately to the economy’s needs. To contribute effectively to the innovation economy and capitalize on the growing opportunities of globalization, India’s young workforce must develop skills that are more market-driven. Given expanding trade and globalization, India’s workforce must have skills that are aligned with its transforming econ-omy and can support the country’s continued economic growth. India’s ongoing but incomplete transformation from an agriculture- to a manufacturing- and services-based economy requires training a workforce with distinct skills for a market that increasingly rewards problem solving, communication skills, teamwork, and self-learning. Skills are needed not only by high-skill sectors but also by labor-intensive industries, which require technological developments to be absorbed by a workforce adept in basic technological literacy and key competencies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".