Strengthening Skills and Education for Innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it