Bibliographic record
Abstract
This Special Issue examines the ways states, regions, de facto states and local actors situated in-between the EU and Russia cope with the competitive external pressure coming from the two regional powers. States, diverse groups and actors in this overlapping neighborhood navigate between varied economic and political integration projects and between different values, ideas and visions of society. This introductory article, first, contextualizes this “inside-out” perspective by presenting the nature of the current EU and Russian projects vis-à-vis the region, how they clash and how this puts regional actors in a state of “in-betweenness”. Then, it unpacks the concept of “navigation” by outlining the ways local actors at different levels of domestic governance studied in the contributions to this Special Issue respond to and manage areas of contestation relating to issues such as citizenship politics, minority rights, and political and trade strategies. The role of elite agency serves as a central thread running across the contributions. Caught “in-between” Russia and the EU, domestic actors, be it at national or sub-national level, navigate while adjusting to the external pressures, negotiating and appropriating external discourses. In the process, constraints are often turned into opportunities for the local actors to exploit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".