Bibliographic record
Abstract
The activation of the startup movement is one of the fundamental preconditions for the transition from innovation to a startup ecosystem, the development of which is impossible without special innovation structures that help startups promote innovative products on the market. The purpose of this article is to modernize the process of promoting innovative products on the market in the form of startups, taking into account the trends of the innovative development of the modern economy. The following methods are used in the article: situational and design approaches; methods of simulation and structural−functional modeling—to determine the potential market demand for innovative products and plan the process of their promotion to the market; and BPMN notation—to formalize the integration links between actors in the process of promoting innovative products on the market. As a result, a scheme for assessing the economic efficiency of innovative product market promotion process management was developed that sorts out several indicators at each stage of the innovation process, which allows one to increase the clarity and completeness of the promotion process management while reducing costs. The system of risk management of innovative products has been studied using the example of the promotion of the innovative startup Hideez Technology Ltd on the market in Europe and the USA. This has allowed the company to benefit economically from implementing the results, reaching USD 20,000. In conclusion, the sequence of actions for making management decisions during the implementation of the strategy for innovative product promotion process management was defined.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".