Социально-Экономические Последствия Повышения Пенсионного Возраста В Российской Федерации // Social And Economic Consequences Of Increase Of Pension Age In The Russian Federation
Bibliographic record
Abstract
The problem of increase of pension age in our country is discussed already from the first steps of market transformations of provision of pensions, i. e. quarter of the century, and without some visible advancement to its decision. At the same time all civilised countries have already developed the accurate program of actions under its decision, though and with different social and economic consequences: one have raised pension age “shock” tools for one-two year, others have stretched this process for some decades, the third have refused an age regulator the insurance pension rights. In present social and economic conditions the problem of increase of pension age in Russia has got a special sharpness. Проблема повышения пенсионного возраста в нашей стране обсуждается с первых шагов рыночных преобразований пенсионного обеспечения, т. е. четверть века, и без сколько-нибудь видимого продвижения к ее решению. В то же время все цивилизованные страны уже выработали четкую программу действий по ее решению, хотя и с разными социальными и экономическими последствиями: одни повысили пенсионный возраст «шоковыми» инструментами за один-два года, другие растянули этот процесс на несколько десятилетий, третьи отказались от возрастного регулятора страховых пенсионных прав. В нынешних социально-экономических условиях проблема повышения пенсионного возраста в России приобрела особую остроту.
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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 | 0.050 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".