Aging Asia and Implications for Social Security Programs
Bibliographic record
Abstract
Abstract The rapidly aging population in Asia, and in most regions of the world, necessitates the provision of social security for larger numbers of older people who are living longer than in the past while also balancing the interests of different generations. This chapter examines how population aging strains existing models of social support and public pension systems in Asia, including a discussion of recent policy responses. Specifically, the chapter analyzes the social security systems in Japan, China, and India as exemplars since these three countries will host a sizable share of the world’s population of oldest old (those 80 and over) in the next few decades. Findings reveal that each nation has pursued unique policy strategies to ensuring income security for its older citizens, but general trends are also apparent. India’s new National Pension is an example of an innovative public policy and administration response in a country without a national social security system. The general trends found in many countries in the region include increases to pension eligibility ages, and consolidating previously separate pension plans. Public administrators in Asia will more and more need to be creative and explore new options given limited resources, and in this regard have much to contribute to promising practices in other parts of the world in adjusting to population aging.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".