The “origins” of Preventing/Countering Violent Extremism: a critical reinterpretation
Bibliographic record
Abstract
This article addresses a void in the history of Preventing/Countering Violent Extremism (P/CVE), whose origins are typically associated with “Islamist homegrown terrorism” in Europe circa 2005. Drawing on genealogical analysis and ethnographic fieldwork, I examine three cases from P/CVE’s pre-history: (1) Dutch security reports beginning in the 1990s conceiving of a “radicalisation process” and multisector prevention; (2) early models in the US depicting the stages of “radicalisation”; and (3) “pre-crime” interventions in Muslim-majority countries that employ similar concepts. I argue that these cases, which have no recorded connection to each other, provide the template for the anticipatory and therapeutic “all-of-society” counter-terrorism approach embodied in P/CVE. I thus challenge the perception that P/CVE originated as a spontaneous and necessary reaction to the “homegrown” incidents in Europe. My investigation raises new questions over P/CVE’s Islamophobic legacy as well as how P/CVE’s turn towards viewing the targets of counter-terrorism as beneficiaries rather than adversaries, as indicated in its pre-history, buoyed the international growth of the field and its expansion across different “types” of violent extremism. The antecedents I explore also call attention towards P/CVE having a sociopolitical function that extends beyond public safety towards suppressing anticipated threats to western sociopolitical orders.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".