Competitiveness of the European Automobile Industry in the Global Context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it