Adaptation of effective international models for regulating the investment activitiesof ENPFSto the conditions of Kazakhstan
Bibliographic record
Abstract
The article is devoted to the legal framework of the investment operation of the Unified Accumulative Pension Fund (UAPF) of the Republic of Kazakhstan. Particular attention is paid to the need for improvement of the current legislation under the conditions of globalization and integration of financial markets. The study offers a comparative analysis of foreign practice in pension asset management in such countries as Norway, Canada, and Australia, where effective models of investment policy have been implemented, ensuring sustainable returns with minimal risks. The possibilities of using these best practices in Kazakhstan’s legal and economic environment are considered. In particular, emphasis is placed on the expansion of the range of investment instruments, the introduction of more liberal asset management company regulations, and the creation of conditions for attracting foreign investors. Special attention is paid to introducing mechanisms for monitoring and assessing the efficiency of investments, including regular audits, risk analysis, and the use of digital technologies. The article also stresses the necessity to raise the level of financial literacy of the population and increase public participation in managing the pension savings. Finally, the article gives recommendations on the modernization of the legal framework, transparency of the investment process, and the elaboration of a complex approach to bringing things into compliance with international standards. These actions, in the long run, should improve the pension system’s financial stability and enhance public confidence in the UAPF.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".