Актуарно-статистические условия регулирования пенсионного возраста в Российской Федерации
Bibliographic record
Abstract
The problem of rising the retirement age in our country has been under discussion since the outset of market reforms of pension systems (i. e. a quarter of a century), and it still has no apparent progress towards a solution. In anticipation of the predicted in the middle of the last century demographic crisis, most civilized countries have embarked on a radical overhaul of not only their social sphere (development of social services, «accessible environment» for people with disabilities, orientation of health care system on gerontological problems, etc.), but also in terms of macroeconomic reallocation of resources according to growing needs of current consumption and maintenance of disabled citizens. The main emphasis is placed on the direct economic stimulation of birth rate. The latter contributed significantly to the leveling of the negative «demographic gaps». However, the whole complex of problems of aging population goes far beyond a simple reproduction of the population. In the current socio-economic conditions, the problem of rising the retirement age in Russia has become particularly acute. The practice of Western countries shows that only when favorable macro-economic and social conditions are created, a positive effect is reached with regards to implementation of institutional and parametric reforms of a pension system in terms of raising the retirement age and the implantation of funded mandatory pension schemes for employers.. This article is the first to present actuarial analysis of demographic, social and labor conditions and prerequisites for rising the retirement age in Russia, which was conducted using official statistics.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".