A Model for Countering the Information and Technical Threats of Intellectual Capital Management of Innovation-Oriented Systems in the Engineering Sector
Bibliographic record
Abstract
The modern economy is characterized by a sharp increase in the role of non-material factors (information and knowledge) in ensuring the competitiveness of individual enterprises and institutions, as well as national economies as a whole. Under these conditions, the ability to create, use and increase intellectual capital is the basis for the economic growth of engineering business entities. The main purpose of the study is to model the counteraction to the main threats of intellectual capital for innovation-oriented systems in the engineering sector of the economy. The relevance of the study is given by the fact that the formation and use of technical, economic, industrial, and other types of knowledge, the totality of which forms intellectual capital, is becoming an urgent problem for modern systems tuned to innovative development. The threats to managing the intellectual capital of innovation-oriented systems are now the most significant, since the achievement of the ultimate goals of the system itself depends on the efficiency of the use of intellectual capital. Taking this into account, intellectual capital is becoming a real and valuable target of criminal encroachments today, which requires scientists to form a clear model for countering these goals. The research methodology involves the use of mathematical methods of information support for the process of counteracting the negative impact of threats. According to the results of the study, we presented the modeling process. As a result, a model was built to counteract the main threats of intellectual capital for innovation-oriented systems in the engineering sector of the economy. Further research requires the construction of a mathematical mechanism for responding to new challenges to the intellectual capital management system for innovation-oriented 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".