Trends in the Architecture of the Most Sustainable Multi-tier Pension Systems
Bibliographic record
Abstract
The article presents a study of the unfunded (distributive) and funded (accumulative) components in multi-level pension systems in different countries around the world. The existing foreign practices of building pension systems are reviewed. It is proved that high sustainability indicators are achieved in those countries where a combination of the unfunded and funded components is used. The relevance of the topic is conditioned by the accrued problems in the pension sector, largely associated with the aging of the population and low birth rate. To conduct the study, the authors turned to countries with multi-tier, most stable pension systems, such as Denmark, the Netherlands, Australia, Sweden, Canada, New Zealand, Finland and Latvia. As a result, a number of features of the pension systems under consideration were identified. Within the state level of pension systems, the amount of payments depends on various criteria and conditions, the use of which is aimed at making payments more targeted. In countries where occupational pension schemes are developed, participation in them is mandatory for employees and employers. At the same time, voluntary pension schemes are used much less frequently by the population of the countries under study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".