Expert Models for the Evaluation of Innovative Entrepreneurial Projects
Bibliographic record
Abstract
In times of crisis development of the economy, when there is less activity of economic entities, changes the structure of assets of organizations. There is a slowdown in innovation and entrepreneurship and innovations (products, processes) also change significantly. So the innovation risks increase dramatically. World experience shows that in periods of financial and economic crises, the most actively introduced innovations that further define the transition to economic growth. Innovative entrepreneurship in the Russian context has always been a highly risky activity. This necessitates the study and systematization of all its components and the final result - efficiency. The last crisis has identified a sharp increase in the risk of innovation development, reduced the probability of success at all stages of innovation, especially for small and medium businesses. However, by themselves these tough market conditions determine the impossibility not only of development, but even simple survival of the organization without innovations that create new business opportunities. Objectively, the growing scale of financial support of the development and commercialization of innovations leads to the fact that private financial support of the Russian entrepreneur becomes insufficient. The need to attract investments and borrowed funds determines the importance of the assessment of innovative entrepreneurial projects before-selling stage. Used in domestic practice, the normative methods for evaluating the effectiveness of innovative projects have drawbacks. In the current environment of uncertainty innovative entrepreneurial project should be considered as a complex system. Its evaluation requires consideration of a significant number of internal and external, quantitative and qualitative factors, and should be conducted by experts as an informal procedure. Expert model for the evaluation of innovative entrepreneurial projects allow us to determine their advantages and disadvantages. Being fairly objective, expert model contribute to the selection of the most effective projects to guide the development of new innovative economy.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".