Bibliographic record
Abstract
Abstract Recent work on borders has tended to overlook border control actors, practices and rationalities in West Africa. States in this region are considered origin and transit countries for irregular migration, and the Sahel region that they straddle is widely seen as an emerging haven of terrorist activity. This article discusses one response to these migration and terrorism threats by the Islamic Republic of Mauritania: a programmme to build new border posts with help from global partners that include the European Union and the International Organization for Migration. The article builds on Bourdieusian approaches in critical security studies, but draws on concepts from actor-network theory to account for the heterogeneity of border control actors and the mobility of different knowledges about how to control borders. Drawing on ethnographic research in Mauritania, the article discusses four ‘actants’ of border security: the border posts, the landscape, the biometric entry–exit system and training practices. Throughout, the article highlights field dynamics of competition, cooperation and pedagogy, also emphasizing the role of non-human agency. The article concludes with a reflection on the link between border control and statebuilding, suggesting that this fusion is a broader paradigm of security provision in the global South.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".