Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the level, composition, and differences in the dynamics of revealed comparative advantage and trade specialization patterns of the 12 new member states (NMS‐12) as part of the enlarged European Union 27 countries (EU‐27). The NMS‐12 are classified into four country groups: the Baltic States, the CEFTA‐5, and the Mediterranean and the Balkan regions. The empirical analysis employs a regression framework, a duration analysis, Markov transition probability matrices, and mobility indices. Trade increases with the EU enlargement and so does revealed comparative advantage in agro‐food products. There are catching‐up difficulties, as indicated by revealed comparative advantage, in higher added‐value processed products. Le présent article examine le degré, la composition et les différences de la dynamique des avantages comparatifs révélés ainsi que les caractéristiques de la spécialisation du commerce des douze nouveaux pays membres (NPM‐12) de l'Union européenne élargie (UE–27). Les 12 nouveaux pays membres sont divisés en quatre groupes: les États baltiques, les cinq pays membres de l'ALECE, la région de la Méditerranée et la région des Balkans. L'analyse empirique utilise un modèle de régression, une analyse de durée, des matrices de probabilités des transitions (Markov) et des indices de mobilité. Les échanges augmentent avec l'élargissement de l'UE tout comme les avantages comparatifs révélés des produits agroalimentaires. On observe des difficultés de rattrapage, comme l'indique l'avantage comparatif révélé, dans le cas des produits transformés à forte valeur ajoutée.
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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 | 0.001 |
| Open science | 0.000 | 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