Socio-Economic Analysis of Disproportions and Disbalances of the Raw Material Export Model in Post-Soviet Russia
Bibliographic record
Abstract
The article provides a socio-economic analysis showing how the export-raw material model of economic growth adopted in post-Soviet Russia affects the socio-economic situation within the country in the context of an unprecedented global recession that is gaining momentum due to COVID-19. As a working hypothesis, the authors propose that the Russian Federation, where the extraction and export of mineral raw materials are the basis of its social and economic growth, has led to numerous production imbalances. This not only lowers the quality and growth potential of Russia's future GDP but also undermines its macroeconomic stability and makes its national economy prone to oil shocks due to the dramatic global recession and lower demand for hydrocarbons. The paper builds a linear regression model to assess the dependence of Russia's GDP on oil exports from 1996 to 2019. Besides, the authors obtained statistically significant regression equations confirming the theoretical assertion that the dependence of the rates of socio-economic growth on the export of natural raw materials reduces the quality and efficiency of state and public institutions since those in power are trying to legislatively facilitate their access to resources, which, in turn, significantly reduces the potential for economic growth. The article confirms the need for a transition to a new (neo-industrial) socio-economic paradigm since this will help overcome the production imbalance that has developed in the Russian economy, ensure long-term socio-economic growth, and increase its efficiency. Proposals are formulated for the formation of economic conditions for neo-industrial economic development, the basis of which should be innovativeness, environmental friendliness, and inclusiveness.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".