Bibliographic record
Abstract
Successive migration ‘crises’ in the Mediterranean have led to renewed scrutiny of search-and-rescue (SAR) logics along the southern European border. This article focuses on the Spanish approach to maritime SAR, which has received relatively less attention than the two other approaches present in the region: the militarised approach and the NGO approach. I use administrative data, budgetary information, and qualitative interviews to discuss the evolution of SASEMAR, the civil and public Spanish SAR agency that has traditionally embraced an expansive interpretation of both humanitarianism and Spain’s legal obligations to protect life at sea. I argue that, with the evolution of the southern EU border, SASEMAR has become an obstacle in the harmonisation of SAR approaches in the region – a process defined by the criminalisation of acts of solidarity not carried or sanctioned by the state, the adoption of a minimalistic interpretation of humanitarianism, and the placing of rescue obligations in the hands of militarised agencies. To remove this obstacle, the Spanish government is reclaiming and re-appropriating SASEMAR’s structure using three main strategies: the precarisation of rescue crews, the territorial externalisation of SAR responsibilities to Morocco, and the transfer of SAR decision-making powers to national, supranational, and international agencies with close links to the military.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".