Peculiarities of Innovative and Investment Policy of Russia and its Regions in the Conditions of Crisis Development
Bibliographic record
Abstract
The article presents the results of comparative analysis of conceptual approaches to understanding the nature and objectives of the national and regional innovation systems in which the prospects for socio-economic development of the country based on innovation and investment decisions are examined. The triumvirate of factors affecting the state of innovation area of Russia is allocated – a series of financial and economic crises, turbulence of the economic environment, geopolitical instability with the consequences of anti-Russian sanctions – and their impact on the economy is interpreted. It is stressed that the speed and scale of the economic transformations indicate the need to adapt the model of innovative development of Russia to the requirements of a sovereign development of the country and its transition to a new technological order. A description of the problems of investment of innovative processes is provided, and new approaches to their solution are opened up, including the implementation of new investment instruments. The necessity, possibility and urgency of an innovative breakthrough of the country is substantiated in compliance with a set of conditions, with the priority given to the formation of a system of strategic management of development of innovative economy that contributes to the identification and implementation of promising directions of economic development.
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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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| 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".