Ageing in India: drifting intergenerational relations, challenges and options
Bibliographic record
Abstract
India, like many other developing countries in the world, is presently witnessing rapid ageing of its population. Almost eight out of 10 older people in India live in rural areas. Urbanisation, modernisation and globalisation have led to changes in economic structure, erosion of societal values and the weakening of social institutions such as the joint family. In this changing economic and social milieu, the younger generation is searching for new identities encompassing economic independence and redefined social roles within, as well as outside, the family. The changing economic structure has reduced the dependence of rural families on land, which had provided strength to bonds between generations. The traditional sense of duty and obligation of the younger generation towards their older generation is being eroded. The older generation is caught between the decline in traditional values on the one hand and the absence of an adequate social security system, on the other. This paper explores the nature and extent of the social and economic pressures that are impinging on intergenerational relationships and discusses the implications for policy towards improving the wellbeing of India’s senior citizens.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".