Optimization and Innovation of China’s Education of the Old Aged Paradigm in the New Era
Bibliographic record
Abstract
The main contradiction in the development of China’s education of the aged in the new era is the contradiction between the growing elder’s longing for a good education life and the inadequate development of the education of the aged. In theory, optimizing and innovating the development paradigm of China’s education of the aged is of great significance and multiplier effect for solving this main contradiction. The first is to clarify the theoretical logic of China’s the aged paradigm optimization (including making clear the inevitability of paradigm optimization and innovation, analyzing the mechanism of paradigm optimization and innovation, reflecting on and finding the existing problems of the current paradigm, and so on.);The second, we must base ourselves on theoretical research and practice frontiers, learn from others, and construct a new paradigm of “Integrate Organism” (including the choice and basis of new paradigm, the specific connotation and development vision of the new paradigm, etc.); The second, it is necessary for us to propose a concrete implementation path with realistic relevance according to the new connotation of the paradigm. Only the overall coordinated promotion of the three can revitalize the vitality of China’s education of the aged in the new era.
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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 | 0.002 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".