INVESTING IN INNOVATION PROJECTS IN RUSSIAâ²S AGRIFOOD COMPLEX
Bibliographic record
Abstract
This paper examines the major aspects of the present-day operation of Russia’s agrifood complex, the state of its agricultural markets, the legal and regulatory framework underlying the sector’s operation, and the nation’s existing interregional trade barriers. The authors bring to light some of the issues related to filling the gaps in the funding of small and medium-sized businesses to ensure boosts in the innovation component and competitiveness of Russia’s agro-industrial complex. Small and medium-sized enterprises within the agro-industrial complex naturally have a pronounced regional orientation. There is a need to activate innovation processes in order to help remediate the sub-par technical and technological condition of the nation’s agricultural sector and food processing industry, insignificant levels of innovation-related activity at science and research institutions, lack of long-term strategy for adapting to changing client demands, and low competitiveness levels within the agrarian sector. A crucial element of policy respecting small and medium-sized enterprises within the agrifood complex is government support for programs financed through budgetary funds. RF legislation has set out specific forms and terms of financial government support for small innovation companies, a key element whereof is a system of funds that are intended to help support innovation and will be employed to finance small-business projects on concessionary terms. In recent years, there has been a continual increase in the number of venture funds with a clear-cut sectoral approach, mainly owing to brisk technological development in the real sector. A great many of these funds have been set up with the participation of state capital.
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 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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".