ELEMENTS OF UNFAIR COMPETITION RUN BY THE USA AND UKRAINE AGAINST RUSSIAN COMPANIES
Bibliographic record
Abstract
The paper analyzes the reasons that provoked unfair competition on the part of the USA and Ukraine against Russian companies in the defense industry market along with the US policy of imposing sanctions on Russia using the situation in Ukraine as a pretext to drive Russia out of the military production market and force the US and Canadian rules of play in this market segment.Subject to discussion are issues of relocation or replacement of the Ukranian enterprises engaged in the production of equipment and spare parts (key parts and components for marine engines, technological equipment for tanks as well as components and assemblies for heavy nuclear missiles) for the Russian military-industrial complex, in particular for the space industry (manufacture of satellite systems and new generation launch vehicles for delivery to the International Space Station (ISS)) aimed at modernization of the military-industrial complex of Russia and revival of its industrial potential [1]. This area of the Russian economy development is viewed as extremely important in today’s increasingly competitive environment in the markets of high-tech manufacturers, and as a way out of the hydrocarbon extraction focused scenario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".