Socioeconomic-Demographic Characteristics and Supporting Resources of the Chinese Elderly
Bibliographic record
Abstract
Inspired by the perspectives of Modernization Theory and Social Stratification Theory of Aging, this paper examines the social resources and the demographic characteristics of the Chinese elderly. The paper addresses the following questions: What social resources are available to the Chinese elderly after retirement? Are the demographic characteristics of the elderly associated with their social resources? Utilizing the 2006 China national survey data, this study demonstrates the current characteristics of the Chinese elderly aged 60 years and above and presents the availability of social resources for the elderly in five categories: external financial resources, medical resources, physical resources, family resources, and self-resources. The study explores the associations between demographic characteristics and the social resources in each area. To complete the above analysis and to test the above theories, methods of statistics of cross-tabulations, Chi-square significance test, and indicators of strengths shown by Cramer’s V and tau-c are applied. The results disclose a comprehensive picture of diverse social resources of the older people in contemporary Chinese society. The findings show that medical and self-resources are most closely associated with the demographic characteristics, followed by family resources, external financial resources, and lastly, physical resources. Policy implications are also addressed.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".