The level of production specialization: Serbia and the new EU member states
Bibliographic record
Abstract
The paper examines the level and changes in production specialization (diversification) characteristic of the manufacturing industry of Serbia and the member states that joined the EU in 2004 and after. The authors aim to analyze the direction of structural changes in Serbia's manufacturing industry and make comparison with the situation in the new EU member states, as well as determine whether those changes that show the same trends as GDP per capita movements are characterized by specialization growth, especially in terms of medium-high and high technology manufacturing activities. Industrial sector specialization index is used to determine the level of specialization of manufacturing industry production sectors and activities. Changes in specialization are analyzed by observing the changes in the mentioned index over a five-year period. The level of specialization of manufacturing sector is compared to the level of GDP per capita and its growth rate. In order to analyze the level of specialization of industry sectors and activities in Bulgaria, the Czech Republic, Estonia, Hungary, Lithuania, Romania, Slovakia, Slovenia and Serbia, the comparison method was used. The results of the research indicate that the direction of structural changes in Serbian manufacturing industry does not follow the usual pattern, i.e., the lower level of GDP per capita results in a higher level of production specialization, while the lower level of specialization and smaller number of activities leads to low technology intensity of production, which is not the case with the new EU member states.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".