Methodical Approach and Tools to Improve the Efficiency of Managing of the Innovation Potential in the Context of Economic Globalization
Bibliographic record
Abstract
The purpose of this study was to develop a methodology for assessing and justifying the tools needed to improve the efficiency of the management of innovation potential in the context of economic globalization on the example of a high-tech enterprise. The studies developed a method of estimation of innovative potential of high-tech enterprise, which differs from the comprehensive approach to the analysis proposed by foreign and Russian scientists, which allows to take into account one aspect of globalization of the economy, and to ensure greater confidence in the conditions of application of expert methods. Proposed methodological approach is based on the methodology of fuzzy set theory, matrix methods of aggregation and analysis of complex systems. The advantage of the proposed in the methodological toolkit is the ability to a coordinated use of indicators which are measured in different difficult comparative values, as well as the transparency of this evaluation. Information obtained through the procedure contains a qualitative and quantitative assessment of each element of the structure of innovative capacity, which is an effective supplement to the management of the organization and allows the supervisor to take justified and high-quality solutions to improve the innovative capacity. The proposed tool, in our opinion, is essential to assess the innovation potential in the analysis of this type in the conditions of uncertainty and incomplete information. Results of the study are universal and can be used for improving the management of all economic systems.
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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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".