Bibliographic record
Abstract
The effect of population growth and demographic transition on economic growth, poverty, inequality, and on rural livelihoods has been widely discussed and debated (Birdsall & Sinding, 2001). Within this debate, the focus has also been on how the age structure of a country's population could affect its future rate of growth. A productive work force coupled with a larger working age population could offer the opportunity for a country to grow faster. This phenomenon is referred to as the demographic dividend. In the context of East Asia, it has been argued that one of the factors contributing to the annual increase in per capita income of over 6 percent over the period 1960–1995 was the favorable age structure of the population. This enabled them to reap the demographic dividend (Bloom & Williamson, 1998). The latest census report suggests that India's average age is 24. Is India ready to reap this benefit? With constant technological innovations, the employment market demand side has been changing dynamically. The optimal strategy for a country like India is to take the dynamic Ricardian comparative advantage framework and constantly train her workforce and be ready to reap the benefits of the population bulge.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".