The Outsourcing of European Migration and Asylum Policy in Niger
Bibliographic record
Abstract
Abstract Situated at the crossroads of sub-Saharan Africa and the Maghreb, Niger has been a departure and transit zone for the past few decades. Following the fall of Gaddafi, the country became a strategic partner to the European Union and its Member States in the search for solutions to the so-called migration crisis. This position was consolidated by its participation in the Valletta Summit in Malta in 2015, during which an emergency trust fund for Africa was set up to stem migration flows from Africa to Europe. Niger is the main beneficiary of the financial mechanism that was put in place, ranging from direct budgetary aid to project funding from United Nations agencies, cooperation and development agencies and non-governmental organisations as well as police cooperation. In return, the country stands out as a reliable partner in the fight against irregular migration and the promotion of the right to asylum. Thus, in addition to the application of Law 2015–36 on Migrant Smuggling, the country innovated with an unprecedented mechanism (the ETM) for the transfer of asylum-seekers and refugees from Libya to Niger, where they are supposed to be resettled in Europe and Canada. In the same year, the UNHCR opened an office in Agadez to facilitate asylum applications in a situation of mixed movements. These actions aim to confine migrants and keep them away from Europe’s borders, against a backdrop of outsourcing of migration and asylum policies. Based on a legal and ethnographic approach, this chapter analyses the organisational changes that have arisen as a result of the EU’s engagement with Niger and the UNHCR in the field of asylum since 2015. The approach is mainly based on two mechanisms introduced after the Valletta Summit: the ETM and the provision of asylum in Agadez in the context of mixed migration. The analysis focuses on the issues of accountability, transparency and compatibility.
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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 | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".