Bibliographic record
Abstract
The ageing of the population is an inevitable social development trend and is presenting many challenges to modern society. The issue of population ageing has involved politics, economy, culture, society and other fields. With the increase of ageing population, the well-being of the elderly will determine the realization of harmonious society. The development of education for the elderly is an inevitable trend of social development. Education for the elderly will contribute to the harmonious development of the whole society. The concept of active ageing is gaining popularity among governments. Continuing education for the elderly in the context of active ageing has many potential benefits. Elderly education can improve the quality of life of the elderly, help them maintain physical functions and productivity for a longer period of time, create opportunities for the elderly to form learning groups, meet new friends, increase social participation, and reduce the feeling of alienation. The development of education for the elderly can also help to develop and use the potential human resources of the elderly and thus promote economic development. The analysis of the benefits of elderly education will draw people’s attention to the importance of the development of elderly education and encourage people from all walks of life to support the further development of education for the elderly. While there are many benefits of continuing education for the elderly, there exist some challenges, one of which is the lack of special teachers for elderly education. The lack of specialized teachers at the Open University for old people can be solved partly by taking advantage of the learners’ lifelong work experience and accumulation of knowledge, and encouraging them to become volunteer teachers. Another solution is to offer various kinds of online courses to facilitate flexible learning at the elderly’s convenience. The development of elderly education will contribute to the active ageing process.
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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".