The European Refugee Crisis: transitioning the EU from state-centric ‘kingdoms’ to a federalist system of multi-level governance
Bibliographic record
Abstract
In an era where European integration has become increasingly questioned and where Euroscepticism battles the objectives envisioned by the Maastricht Treaty of 1992, the European Union (EU) desperately needs to revitalize its project of unification if its hopes to survive. Events of the last decade, such as the sovereign debt crisis, the global financial crisis, and the evolving refugee crisis, have challenged the efficacy of the EU and have seemingly undermined its legitimacy as a regulatory body. Taken individually, these crises pose a potent threat to the success of European integration and to the enlargement of member state unification. Most recently, the ongoing refugee crisis has created a sense of disunion within the EU giving way to a state of calamity as successive European efforts have failed at resolving this issue. Reeling from civil conflict and political turmoil, individuals from various regions, most notably Africa, the Middle East, and South Asia, have fled the dangers and uncertainties of their homes in order to seek refuge within neighbouring European countries. This arduous and sudden development has prompted commentators, such as former Greek finance minister Yanis Varoufakis, to claim that the solidarity of the EU is being threatened at a level not seen since the migrant crisis of 1945 during the Second World War.[...]
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".