European Biometric Border System, Securitization and (Im)mobilities in West Africa
Bibliographic record
Abstract
This article interrogates the European biometric ID system and securitisation measures in West African borders which have become detrimental to, first, African migrants and, second, both African and European security objectives. Using the Niger’s experience, we demonstrate how migrants’ identity problems as well as their atomisation and loosening of their social integration are directly linked to the criminalising and dehumanising border security practices they now face. This article reveals the multiple forms and effects of the unimpeded European biometric/digital control over African territorial borderlands and (im)mobilities. First is the subversion of African states’ administrative, decisional, sovereign and territorial prerogatives by way of enacting digital territorial borderscapes that enforce migrants’ identity de(re)construction. Second, the use of ‘biometric power’ to facilitate a specific modality of neoliberal biometric power relations which perpetuates global inequalities in biometric identification and (im)mobility governance. Lastly, migrants’ recourse to agentic mechanisms to contest the European biometric ID system, via discoveries and implantation of parallel border routes.
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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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".