How to overcome poverty and ensure sustainable growth of the middle class: criteria of distribution and policy measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article explores multicriterial approaches to determine the boundaries of poverty and the middle class. Applied regression analysis confirms the significance of some households’ social and economic characteristics that increase the likelihood of their belonging to a certain population group. Based on various methodological approaches, the analysis of the Russian society structure reveals its high polarization and a significant share of poor population fluctuating from 12% to a quarter of the population and even more considering the parameters that determine the quality of life, peculiarities in behavior and self-identification. Applying the multicriterial approach, the authors propose additional measures to support citizens which cover not only the poorest segments of the population and large families, but also a significant cohort of low-income citizens, as well as the measures that contribute to higher-income work and the increment of human wealth.The article explores multicriterial approaches to determine the boundaries of poverty and the middle class. Applied regression analysis confirms the significance of some households’ social and economic characteristics that increase the likelihood of their belonging to a certain population group. Based on various methodological approaches, the analysis of the Russian society structure reveals its high polarization and a significant share of poor population fluctuating from 12% to a quarter of the population and even more considering the parameters that determine the quality of life, peculiarities in behavior and self-identification. Applying the multicriterial approach, the authors propose additional measures to support citizens which cover not only the poorest segments of the population and large families, but also a significant cohort of low-income citizens, as well as the measures that contribute to higher-income work and the increment of human wealth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it