Special Issue: Resilient states versus resilient societies? Whose security does the EU protect through the Eastern Partnership in times of geopolitical crises?
Bibliographic record
Abstract
The European Union (EU) is making strong inroads into areas of security traditionally reserved to states. Security concerns are increasingly triggered by fundamental challenges, such as terrorism, climate change, migration, and many other 'soft security issues'. Resilience has become a term of reference in the EU's official foreign policy discourse, triggering an associated 'resilience. Our contribution aims to analyse the 'many faces of resilience' in the EU's Eastern Partnership (EaP) in relation to how the EU understands and seeks to enhance European security, mapping the different meanings the terms assume in the EU's discourses and policy practices, and how they are related to one another. The Russian invasion of Ukraine in February 2022 has largely been viewed as an extraordinary resilience test for the EU and has brought back fundamental concerns on European security, including 'hard' military security issues. This has in turn raised questions not only on how the EU can ensure the resilience of its eastern partners and of itself, but also on the EU's role in a rapidly changing global context of polarisation and fragmentation. In light of these challenges, the contributions to this special issue have only increased in relevance, pointing to pathways and opportunities for how the EU may reconcile the contradictory demands of fostering security and resilience for states and societies alike.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".