Conditions for the Growth of the “Silver Economy” in the Context of Sustainable Development Goals: Peculiarities of Russia
Bibliographic record
Abstract
A “silver economy” can drive economic growth. The key condition is effective demand, determined by the number of financially secure members of the elderly population. The aim of this study is to assess the conditions of the Russian “silver economy”, identify the constraints on its growth, and develop recommendations for their elimination to achieve Sustainable Development Goals. We applied multivariate statistical analysis methods. The absolute and structural numbers of elderly people in Russia were found to not differ much from those in the developed countries of Europe. Their financial support exhibits several important features. A state pension plays a key role in financing the needs of Russian pensioners. Income from labor occupies the second position. Asset-based reallocations are negligible. Public programs will improve the standard of living of current pensioners. For future pensioners, it is important to increase the income received from asset-based reallocations. Russian pensioners were found to have had a negative experience of participation in the funded pension system. It is necessary to stimulate the voluntary participation of future pensioners in the funded pension system and to change the regulation of the investment activities of pension managers. In general, the formation of conditions favorable to the “silver economy” may turn it into a driver of sustainable development in Russia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".