Bibliographic record
Abstract
Digitalization of the economy, increasing the efficiency of management of socio-economic systems, in turn, makes them more demanding. This is especially true for the level of differentiation of the main macroeconomic characteristics of the country's regions, since only in the case of their unification can a single digital space of Russia be formed. Using econometric methods, the article assesses the degree of differentiation of the average per capita GRP of the Russian regions and compares the coefficient of variation of this indicator with a similar indicator characteristic of the largest and most developed countries. As a result, it was found that the level of interregional differentiation of per capita GRP in Russia significantly exceeds similar indicators in all countries included in the study - Canada, USA, China, Brazil, Australia. It should be noted that interregional differentiation of the level of socio-economic development is characteristic of all large countries, which is due to the territorial variation of macroeconomic and natural-climatic characteristics. However, in Russia this process is becoming disastrous, which is due to the fact that the country's economy is still oriented towards the extensive exploitation of natural resources. Thus, the digitalization of the economy should become an important tool to reduce the level of socio-economic differentiation of the regions of the Russian Federation, since it involves the creation of a large number of highly paid jobs and the predominant use of intellectual capital.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".