Analysis of foreign experience in stimulating innovation activities of small and medium-sized industrial enterprises as an element of strategic development of the state
Bibliographic record
Abstract
В статье представлен анализ зарубежного опыта стимулирования инновационной деятельности малых и средних промышленных предприятий как элемента стратегического развития государства. Особое внимание уделено стимулированию инновационной деятельности промышленных предприятий стран Европы (Германия, Великобритания, Финляндия), Америки (США, Канада) и Азии (Япония, Китай, Южная Корея, Индия). Сделан вывод о том, что изученный опыт различен, имеет общие черты и особенности, а также может быть применен при разработке стратегии инновационного развития России. Выделены главные и основные особенности всех изученных методов. The article presents an analysis of foreign experience in stimulating innovation activities of small and medium-sized industrial enterprises as an element of strategic development of the state. Special attention is paid to stimulating innovative activities of industrial enterprises in Europe (Germany, great Britain, Finland), America (USA, Canada) and Asia (Japan, China, South Korea, India). It is concluded that the studied experience is different, has common features and features, and can also be applied in the development of a strategy for innovative development in Russia. The main and main features of all the studied methods are highlighted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".