Bibliographic record
Abstract
The paper studies the problem of poverty in modern Russia and identifies the main approaches to measure poverty. The paper shows some limitations of objective poverty indicators and proposes to wider use of subjective poverty indicators based on self-assessment of well-being. It presents estimates of subjective poverty in Russia for the period from 2003 to 1-st quarter of 2015. It is shown that the poor are found mostly among retirees, low educational level persons engaged in low-skills jobs, as well as women and military personnel. The paper mentions the cities and towns where in early 2015 the share of the poor population reached a rather high level. Nevertheless, it is concluded that the crisis has not led to a significant increase in the share of poor and low-income Russians. There is no reason to say that the increase in poverty due to the crisis threatens the social stability in Russia. The material presented in this paper may be of interest to the public authorities at the federal and municipal levels to monitor the level of poverty and to plan social measures to combat it.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.141 | 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; 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".