Balance and Stability of Polish Pension Insurance System
Bibliographic record
Abstract
The structure of the Polish pension insurance system, despite many reforms carried out in recent years, is still mainly based on the pay-as-you-go (repartition) pillar. To make it work properly, a constant inflow of participants who will pay contributions, thanks to which it will be possible to pay benefits to current beneficiaries, is necessary. At the same time, a negative demographic trend is observed, which can be a signal that more and more people are going to be paid from the system, while fewer people are going to provide money to it. Therefore, the question arises: How much time is there left for repartition-based pension insurance system to last? Is this system really a vehicle of economic and social development or retrograde rather? This article is an attempt to answer such question using the example of the Polish pension insurance system (PIS). To answer this question, linear trend models were used in the analysis. The adjustment of these models to reality was high, and on their basis, the forecasts for the following years were estimated. The variables used in the analysis are time, number of people, and the value of contributions and withdrawals. According to the research, it can be concluded that Polish pension insurance system has about 60 years to last in such form. Demographic changes are definitely unfavorable, and the age gap is getting bigger and bigger. This means that fewer people are going to provide money for those who are inactive.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".