The Ceuta Border Peripeteia: Tasting the Externalities of EU Border Externalization
Bibliographic record
Abstract
On May 18th 2021 more than 8000 people irregularly crossed the EU external border between Morocco and Ceuta. Morocco was accused of not acting diligently enough to prevent this unprecedented influx. Interestingly, this occurred in a geopolitical context of rising tensions within Spanish-Moroccan diplomatic relations-triggered by Trump administration’s recognition of Moroccan sovereignty over the Spanish former colony of Western Sahara in 2020. Things reached a peak of complexity when Brahim Ghali, the secretary-general of the Saharawi Polisario Front, travelled to Spain in order to receive treatment for COVID-19 in April 2021. In this light, this contribution argues that, what happened in Ceuta in May 2021 constitutes a handbook example how to manufacture a border/migration “crisis” for foreign policy purposes. The text interrogates the limits and costs of increasing foreign reliance vis-à-vis EU migration and border management policies. And in so doing, it points at two mutually reinforcing consequences of outsourcing strategies, here referred to as the “externalities of externalization”. On the one hand, the growing diplomatic leverage at the disposal of neighboring gatekeeper-countries like Morocco or Turkey; and, on the other hand, the EU-wide electoral growth of far-right, anti-immigration political discourses advocating for even more strictly securitized border practices.
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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 | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".