World experience in using the program-targeted planning methods for high-tech industry development
Bibliographic record
Abstract
Subject. The article investigates the program-targeted planning methodology, which is implemented in the Russian Federation and leading foreign countries, for high-tech industry development. Objectives. The aim is to identify the specifics of program-targeted planning for the development of high-tech industries, to shape programs and plans for innovative development in the Russian Federation and leading foreign countries. Methods. The study employs general scientific methods of systems analysis, including the statistical and logical analysis. Results. I reviewed methods of program-targeted planning, implemented by the world’s leading countries (the Russian Federation, United States of America, France, Great Britain, Netherlands, Norway, Japan, Canada), in the interests of the development of various high-tech sectors of the economy. The study established that the methodology of program-targeted management is an effective tool for resource allocation by various types of economic activities in accordance with national priorities. I developed proposals by priority areas for improving the methodology for program-targeted planning and management in the Russian Federation in modern economic conditions. Conclusions. The findings and presented proposals can be used to improve methods for program-targeted planning to develop high-tech sectors of the economy; to design various long-term programs and plans, reducing the risk of their implementation; to determine the ways and methods of sustainable socio-economic and innovative and technological development of the world's leading economies.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".