Pension funds in the context of globalisation: Legal regulation and challenges in countries with different pension systems
Bibliographic record
Abstract
The purpose of the study was to identify key issues in the legal regulation of the pension fund of the Kyrgyz Republic and to develop proposals for their effective resolution based on the analysis of international practices in various economic systems. The study involved an analysis of legislation using the formal-legal method, a comparison of pension systems in Germany, Sweden, Chile, and Canada through the comparative method, and modelling potential development scenarios for pension legislation using legal forecasting. The main findings demonstrated that the pension system in Kyrgyzstan remains reliant on state sources, limiting its capacity to adapt to changing international financial conditions. The primary challenges in legal regulation included the lack of flexible investment mechanisms and weak control over asset management. Comparative analysis also revealed that countries with diversified pension systems, such as Germany and Canada, pension systems are more resilient due to asset diversification and the utilisation of private investment funds. In countries like Sweden and Chile, there is a growing interest in private pension savings and the implementation of digital technologies for managing pension assets, contributing to greater transparency and efficiency in pension systems. Based on the analysis of international practices, recommendations were developed to strengthen the legal regulation of the Kyrgyz pension fund, including the adoption of international standards of financial transparency and the development of mechanisms to attract private investments into pension assets. The findings indicated the necessity for modernisation of the legal regulation of pension funds in Kyrgyzstan, considering international practices to ensure resilience, transparency, and the protection of participantsʼ rights
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".