“Still a bit uncomfortable, to be an arm of the state”: Making sense and subjects of counter-extremism in the UK and Morocco
Bibliographic record
Abstract
Countering violent extremism (CVE) policies infiltrate every corner of public life, travelling across the Global North and South. However, scholars have under-analysed the perspective of those charged with CVE's implementation, and have treated CVE in a spatial binary, implying that its operationalisations in the Global North and South are conceptually distinct. This article presents a comparative political ethnography of CVE projects framed as care provision in the field of education in Morocco and the UK. It asks, how is CVE rationalised for and by non-traditional security actors in education, such as university and NGO administrators, and how is it integrated into the ordinary across the North and the South? In both contexts, implementation does not "just" enrol those involved with care duties at their institution into the government of the "dangerous other." It also shapes the self-governance of those transformed into hesitant security actors. This paper argues that implementers leverage the 'normal politics' of institutional care to implement the global counter-extremist agenda. CVE enters spaces of education globally through camouflage - it blends itself into existing understandings and practices of institutional care, whatever they may be. By working across the North and the South through similar mechanisms of sense and subject-making, CVE recruits implementers for the co-production of an expansive global geography of exclusion that locates marginalised young Muslims as global outsiders within.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.031 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".