Militarism and its limits: Sociological insights on security assemblages in the Sahel
Bibliographic record
Abstract
Abstract This article assesses the concepts of militarism and militarization in relation to contemporary security interventions in the Sahel, a region increasingly understood through the prisms of violence, cross-border illicit flows, and limited statehood. This region is subject to security interventions that include French military action, EU-funded projects to prevent drug trafficking, and both bilateral and multilateral efforts against irregular migration. To many observers, it is experiencing an ongoing militarization. We argue that while the inextricable concepts of militarism and militarization go some way towards explaining interventions’ occasional use of military violence, they are limited in their grasp of the non-martial and symbolic violence in security practices. We instead propose a focus on assemblages of (in)security to show the heterogeneous mix of global and local actors, and often contradictory rationalities and practices that shape the logics of symbolic and martial violence in the region. Throughout, the article draws on the authors’ fieldwork in Mauritania, Senegal, and Niger, and includes two case studies on efforts against the Sahel’s ‘crime–terror nexus’ and to control irregular migration through the region. The article’s contribution is to better situate debates about militarism and militarization in relation to (in)security and to provide a more granular understanding of the Sahel’s security politics.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".