Integrative Organized Hypocrisy? Normative Contentions within the EU and the Refugee Migrant Crisis
Bibliographic record
Abstract
In 2015 and 2016, 2.3 million individuals applied for asylum in Europe, the highest number since the creation of the EU. The unprecedented strain on the Common European Asylum policies (CEAS), along with the asymmetric pressure on external border countries and the lack of unified support for border controls, highlighted the tensions between member-state sovereignty and regional competence. According to Lavenex (2018), the Refugee and Migrant Crisis (RMC) was first and foremost a crisis of governance, expressing doubts about the EU’s ability to “fail forward” into further integration in the long-run because of “organised hypocrisy”, an unintended organisational strategy deployed to cope with otherwise irreconcilable differences between normative aspirations and real-life actions concerning asylum. This article revisits Lavenex’s premise of European governance and organised hypocrisy and argues for a more optimistic outlook on European integration. Using the infrastructural Europeanism framework as identified by Pelizza and Loschi (2023), this article argues that despite the legal and legislative gridlocks that surround important issues such as asylum, European integration in relation to asylum is ‘failing forward’ in no small part due to organised hypocrisy and not in spite of it.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".