Producing the ‘transit’ migration state: international security intervention in Niger
Bibliographic record
Abstract
Despite a growing interest in transit migration and border controls along migration routes, there is relatively little work on the production and operation of the category of ‘transit’ itself. This article investigates how Niger emerges as a country of migration ‘transit’ and what impacts this categorisation has had on security and development interventions targeting the country. Building from the literature on the governance of transit migration and on the ‘migration state’, this article theorises transit as a political label. It argues that Niger’s status as a transit country is constructed through a ‘polyvocal’ process involving the discourse and everyday assumptions of international and local actors. The article locates this shared understanding in official texts, everyday routines, and sub-state diplomatic practices. It goes on to argue that these framings, despite divergent rationales, have effects visible in the evolution of security intervention in Niger. These include shifts in the location of border security, the blurring of migration into other transnational threats, and the creation of new domestic institutional practices. The article contributes to theorising the political construction and specificity of transit-ness and provides a fresh case for the research agenda on inter-state relations around migration governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".