Optimization Model of Structural Allocation of Financial Resources in the Pension System of Ukraine
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Bibliographic record
Abstract
The article considers special conditions for the formation of the optimization model of structural allocation of financial resources in the pension system of Ukraine. A methodological approach to determining the optimal structural allocation of financial resources in the pension system is developed. The economic-mathematical model of pension provision is derived. The dependence of the optimal rate of contributions to the pension system. An algorithm for calculating the share of the capital of the pension system in the economic potential of the state using the "golden rule" of the balance of pensions is formed. Models of optimal distribution of financial resources over time between the generation of pensioners and able-bodied persons, as well as a model of parameters of state pension policy have been formed. The number of pensioners by type of pension, as well as the conditions of payment of pensions through banking institutions of Ukraine are analyzed. The coefficient of coverage of old-age pensions in Ukraine and EU countries has been determined. The coefficient of replacement of pensions by age, disability and in case of loss of a breadwinner in Ukraine and the countries of the world is modelled. The indicators of the accumulative component of the pension system of Ukraine are substantiated. It is proved that the combination of distribution and accumulative pension systems will allow investing pension funds, determining the criteria for optimizing the investment process in developing a mechanism for allocating financial resources of the pension system of Ukraine as pension savings.
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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 it