Bibliographic record
Abstract
В статье представлены результаты сравнительного анализа социальноэкономических, демографических характеристик городских и сельских пенсионеров, включая субъективные оценки уверенности в ближайшем будущем. Исходные данные представлены информацией Росстата и проекта «Российский мониторинг экономического положения и здоровья населения (RLMS-HSE)» за 2020 год. Сельских пенсионеров характеризует более низкий размер пенсии. Оплата труда работающих пенсионеров на селе также ниже, в результате и денежные душевые доходы в семьях с пенсионерами на селе почти на четверть ниже городских, что отражается в более высокой степени неуверенности в ближайших жизненных перспективах. Формирование политики активного долголетия российских пенсионеров, направленной на улучшение качества жизни граждан старшего поколения, предполагает выравнивание возможностей для жителей города и села. Необходима дифференциация пенсионной политики, учитывающая специфику занятости и условий жизни на селе, разработка мер для повышения уровня жизни сельских пенсионеров. The article presents the results of a comparative analysis of socio-economic, demographic characteristics of urban and rural pensioners, including subjective assessments of confidence in the near future. The initial data are presented by information from Rosstat and the project «Russian Monitoring of the economic situation and public Health (RLMS-HSE)» for 2020. Rural pensioners are characterized by a lower pension. The wages of working pensioners in rural areas are also lower, as a result, monetary per capita incomes in families with pensioners in rural areas are almost a quarter lower than urban ones, which is reflected in a higher degree of uncertainty in the immediate life prospects. The formation of a policy of active longevity of Russian pensioners, aimed at improving the quality of life of older citizens, involves equalizing opportunities for residents of the city and village. It is necessary to differentiate the pension policy, taking into account the specifics of employment and living conditions in rural areas, the development of measures to improve the standard of living of rural pensioners.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".