Innovative Levers for Ensuring the Polyvector Development of Enterprises: the Experience of Economically Developed Countries
Bibliographic record
Abstract
The article is aimed at generalization of experience of the economically developed world countries in the sphere of formation and use of innovative levers for ensuring the polyvector development of enterprises. The expediency of consideration of this issue has been substantiated in view of the low level of efficiency regarding the innovation policy in Ukraine. The main innovative initiatives of the EU, implemented in the recent years and accepted as effective for intensifying innovation activity of integrative education, have been considered. Attention is drawn to the greater support to small and mediumsized enterprises in the EU towards the development of their innovation activities. The major projects in the European Union, Canada, China, Japan and the United States, aimed at the development of scientific research and implementation of innovation technologies, has been reviewed. The importance of venture capital and business «angels» for financing the risky innovation projects in the economically developed world countries has been emphasized. The role, meaning and practical significance of formation and development of the innovation infrastructure to improve innovative levers for ensuring polyvector development of enterprises has been disclosed. Prospects for further researches on the topic should be consisted in a detailed consideration of the conditions and opportunities for use of the above-mentioned innovative levers in terms of the economy of Ukraine.
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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 | 0.003 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".