Russia in the world market of aircraft engines: Problems and prospects
Bibliographic record
Abstract
The aviation industry is one of the economy’s most knowledge-intensive and innovative sectors. For this reason, the main civil aviation manufacturers have a full production cycle for creating aircraft. A limited number of countries represent them. These are the USA, France, Germany, Great Britain and Spain, as well as Russia, Brazil, Canada, and China. Boeing and Airbus are the undisputed leaders in the international civil aviation market. Companies from the USA (General Electric, Pratt Whitney) and Europe (Rolls-Royce, Safran) are also leading in the aircraft engine market. After a protracted recession, the aircraft industry in Russia began to integrate into the global aviation industry successfully. But, the restrictions imposed in the spring of 2022 against Russian civil aviation have impacted the possibilities of its development within international production value chains, significantly changing plans for individual projects and the Russian aviation industry as a whole. The goal of the article is to determine the place and prospects of Russia in the world market of aircraft engines; identify the possibilities of domestic enterprises to quickly implement measures to transfer all aircraft systems and units to domestic analogues. The article gives a general description of the global civil aircraft industry, including the production of aircraft engines. Leading companies in the global aircraft manufacturing market are represented. The study results made it possible to determine the main trends in this market; identify factors and conditions that influence their formation. In this context, the role of import substitution in this area of activity, the problems of the Russian aviation industry and its ability to provide the domestic market with civilian airliners in the foreseeable future are analyzed.
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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 | 0.000 | 0.000 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".