Eastern Dimension of the European Neighbourhood Policy: Europeanization Mutual Trap
Bibliographic record
Abstract
The article analyses the Europeanization policy of the European Union towards the Eastern Partnership participant countries. Suffering from the lack of clear strategy and ultimate goal in the European Neighbourhood Policy the European Union enhances external democratization and its governance in post soviet states without immediate Union's membership perspective. Underestimation of common neighbourhood geopolitical duality in the context of growing rivalry between European (EU) and Eurasian (Custom Union/Eurasian Economic Union) integration gravitation centers presents the Eastern partners of the EU with a fierce dilemma of externally forced immediate geopolitical and civilizational choice while not all of them are well prepared to such a choice. The mutual Europeanization trap here to be studied both for the EU and its Eastern partners (involving Russia) is a deficiency of regulating cooperation mechanism in the situation of European and Eurasian free trades zones overlapping. Vilnius Summit 2013 results test the "European aspirations" of the New Independent States and upset the ongoing process of the European Neighbourhood Policy in the context of growing economic interdependence in Wider Europe. Besides, the Ukrainian crisis escalation during 2014 as a new seat of tension provokes unbalance of the whole European security system and creates new dividing lines in Europe from Vancouver to Vladivostok.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".