Bibliographic record
Abstract
During the first few years of the post-communist transition, the value of pensions fell, and pension arrears appeared in parts of Russia in the mid-1990s. The legislature focussed much attention on the effort to achieve regular pension indexation, as well as to call for a more transparent pension funding system. Eventually, the executive heeded these calls, and Prime Minister Putin is generally credited with ending the pension arrears crisis in 1999. In 2001, the government proposed a major pension reform which was passed by the Duma. 1 But meanwhile, although politicians were quite interested in the plight of Russian society’s senior citizens, experts warned as early as the mid-1990s that the youngest members of society – children – were in peril. By the mid-1990s, 44 per cent of Russians lived under the poverty line. 2 Statistics from 1996– 1998 showed that families with children were especially likely to live in poverty and to stay in poverty throughout the period. 3 The number of children attending school declined by almost three percentage points between 1990 and 1997. 4 And yet children received relatively little attention from the Russian legislature until the late 1990s. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 |
| 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 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".