The Impact of the Implementation of the Welfare State Concept on the Level of Poverty in Russia and Norway
Bibliographic record
Abstract
At present, the problem of poverty is urgent for both Russia and Norway. According to Federal State Statistics Service of the Russian Federation, 12.1% of the Russian population was below the poverty line in the second quarter of 2021. According to the World Bank, the national poverty rate in Norway in 2018 was 12.7%. At the same time, both states position themselves as having overcome extreme poverty. Both states use the social-democratic type of the welfare state concept as the basis of social policy. The purpose of this study is to research the influence the welfare state concept application on the national level of poverty in Russia and Norway. The research methods are the analysis of official statistics of Russia, Norway and the World Bank, international reports, legislative acts and the analysis of media texts. The conclusions of this study highlight that the use of the welfare state concept in the Russian Federation and the Kingdom of Norway can positively affect the national level of poverty, but it contains a set of significant risks. The results show that further use of elements of the welfare state concept to combat poverty is possible in both countries, but taking into account the current realities, namely, the application of the principles of multidimensional evaluation, targeting in implementation and consideration of regional specificity in practical work with the phenomenon of poverty, and the involvement of other (besides the state) social institutions in this process. The conclusion of this paper formulates recommendations for state and municipal authorities of the Arctic subjects of the Russian Federation (mainly) and the Kingdom of Norway (to a lesser extent) to adjust the social practices used with regard to current trends and taking into account the identified risks.
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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 | 0.008 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".