Competitiveness of the European Automobile Industry in the Global Context
Bibliographic record
Abstract
Abstract The automobile industry is one of the most rapidly growing industries, a significant employer and investor in research and development, and also one of the most important sectors of the EU economy. Nevertheless, even this sector has gone through a series of structural changes and territorial transfers, recently. Exactly for this reason, it seems crucial to examine the competitiveness of the automobile industry on the national level, analyze the long-term trends throughout the whole EU, and put them in a global context. The article uses standard methods of statistical analysis of indices of revealed symmetrical comparative advantage to detect the trends characterizing the shape and long-term development of the automobile industry in Europe. The authors point out the substantial shift s in production and exports from traditional Western European car makers in favor of the new EU member states, but also from the USA and Canada in favor of new, fast-growing developing countries in the South and Southeast Asia and in Latin America. A brief outline of the European Commission’s response to these changes in the European automobile industry in the form of an Action Plan CARS 2020 can be found in the final part of the article.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".