Research on the Support Mechanism for High-Quality Development of Elderly Education in Higher Education Institutions Comparison and Reflection Based on Elderly-Friendly Universities
Bibliographic record
Abstract
Against the backdrop of accelerating population aging and a progressively declining birth rate, higher education is facing unprecedented challenges and opportunities. To actively address demographic changes, the first priority should be given to elderly education. The concept of the “Age-Friendly University” ( AFU) as an institutional innovation to address the challenges of an aging society is gaining widespread attention and practical application globally. Based on case studies of three representative universities—Dublin City University, the University of Manitoba, and the University of Strathclyde—this paper analyzes their AFU implementation pathways in terms of institutional building, educational provision, organizational mechanisms, and cultural cultivation, identifying their common characteristics and diverse development models. Considering Chinas policy context for elderly education and the practical foundation of higher education institutions, this paper further proposes pathways for promoting the participation of Chinese universities in elderly education, including building a consensus on action, developing diverse educational provision, creating an intelligent ecosystem, and establishing a collaborative governance mechanism. The aim is to provide theoretical support and practical references for establishing a higher education system that serves older adults.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".