Multi-levelling and externalizing migration and asylum: lessons from the southern European islands
Bibliographic record
Abstract
Southern European countries have come to constitute the most vulnerable external border of the European Union (EU) over the last decade. Irregular migration pressures have been acutely felt on the EU’s southern sea borders, and particularly on four sets of islands: Canary Islands (Spain), Lampedusa and Linosa (Italy), Malta, and Aegean Islands (Greece). This quartet is, to a large extent, used as stepping stones by irregular migrants and asylum seekers to reach the European continent. This paper studies the role of these islands as ‘outposts’ of a framework of externalization. It starts by discussing the notion of externalization and its different facets. It considers how externalization is linked to both fencing and gate-keeping strategies of migration and asylum control. The second part of the paper focuses on the special role of the island quartet with respect to the externalization web cast by national and EU-wide migration policies. It concludes with a critical reflection on the multi-level character of externalization policies and practices that occur both within the EU and between the EU and third countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".