Constructing the Mediterranean Region: Obscuring Violence in the Bordering of Europe’s Migration “Crises”
Bibliographic record
Abstract
Many names have been attached to regional spaces of migration around the edges of the European Union, including the Mediterranean, Africa-Europe, EU, and Schengen. These regional distinctions and the image of contiguous boundaries assume certain territorial stabilities that can be known, mapped, and policed: the African continent, the European Union, the Mediterranean and even the notion of territorial waters. Yet, territoriality itself is an unstable concept, and the many crises unfolding in the interstitial spaces in the Mediterranean signal precisely the fluidity of the region. Regional solutions are popular within the current panoply of enforcement strategies used to manage migration, but they function to reify and stabilize the concept of the region and obscure violence happening at other scales. In this paper we build on political geographers’ examinations of the social construction of scale to investigate the ways in which the region has been created through “migration management.” Building on the work of feminist geographers, we contend that attention to the scale of the migrant body shows the violence obscured by regionalized migration management and opens up spaces and strategies of political engagement. This approach highlights the multiple places where the EU-Africa borderlands are constructed and shifts the conversation from a state-centric discourse of migration management enacted at the region to one of embodied migration politics that addresses violence transpiring at finer scales.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".