Bibliographic record
Abstract
This article argues that border management practices in the Sahel, strongly driven by European concerns and funding, are reaching into new geographic and policy areas. It introduces the concept of borderwork ‘creep’, to highlight how border management practices have, in the last 15 years, expanded along three axes. This term borrows from debates on ‘function creep’ in surveillance studies which have generally been focused on digital technologies in the global north. The first axis is a cartographic one, with borderwork functioning through a denial of cartographic limits to border security. Borderwork has crept inland, with security practices taking an expansive vision of the borderland and bringing controls to key inland nodes. The second is that of a cross-pollination of policy areas with a growing role for judicialised modes of justification for borderwork. The third relates to faith in technology, with new digital geographies making borderwork in the Sahel reliant on data handling and sharing. To make these arguments, the article draws on fieldwork since 2013 in Senegal, Mauritania, and Niger. By examining the breadth and expansion of borderwork in the Sahel, it contests visions that centre ‘Fortress Europe’ and instead highlights the multiple often overlapping global and local interests that expand borderwork in the Sahel.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".