Sources and Sociology Concerns of Financing the Innovation Activities in Russia
Bibliographic record
Abstract
The innovation abilities of an enterprise and the feasibility of an innovative project always depend on financing. We can say that the main issue is the assessment of funds required for the implementation of an innovation and the analysis of their possible sources. In this paper, different mechanisms of financing and allocation of financial resources and their impact on innovative performance were examined. In general, financing sources of the institutes in different countries have adopted different mechanisms to provide and allocate resources from the range of public funding to private financing. But the amount varies from country to country so that in European institutions most government funding and the United States, private financing is the predominant form of financing. Some governments subsidize the supply side (higher education institutions), some on the demand side (customers), and some on both sides of the higher education services market. In general, negotiation-based and formula-based allocation, personnel-based allocation, student-based allocation, per capita cost, priority-based, and performance-based allocation are among the mechanisms used in this regard. The results of this article showed that the mechanisms of allocating and allocating resources in higher education play the role of policy-making and guiding the behavior of actors and can affect the performance of universities and institutions of higher education at the macro and micro level (individual). In the area of funding, graduates should contribute to the financing of universities, and in the area of allocation, performance-based allocation mechanisms should be used to achieve greater efficiency, accessibility, and equity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".