Bibliographic record
Abstract
The last stage of demographic transition seems to have absorbed the world into its clasp. The population growth rate may be falling; but the aggregate numbers over the globe are frightening. According to estimates1 the population over the globe will raise from 6.9 billion presently to 9.5 billion in 2050, a rise of massive 2.6 billion in mere four decades. The seriousness of the issue can be felt from the fact that proportion of population aged 60+ will account for 22% of World's population, with a massive 33% living in developed regions. The similar share for India by 2050 will stand at 20%, 60% of India's population will be in the age group of 15-59 and a median age would be still in 30's. Thus, the demographic dividend will continue to provide immense competitive advantage to India by 2050. The rising proportion of elderly will create Socio- Economic burden on the present generation. Financing towards pension, health care and other social well being of the former generation will be quite a challenging task. Albeit; this paper attempts to look at the flip side of the ageing crisis, for any country the ageing and demand patterns are always correlated. High volume of elderly population generates profitable opportunity to supply customized goods and services targeting them. Young countries like India can look forward for tremendous opportunities in labor market, service industry and other consumer goods industry mostly in Japan, Australia, Canada, Europe and other ageing economies by concentrating on consumption needs of older people. The paper shall analyze vital demographic indices of select countries and find how India can take commercial advantage of such ageing economies and realize its demographic dividend.
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 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 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".