Security Traps and Discourses of Radicalization: Examining Surveillance Practices Targeting Muslims in Canada
Bibliographic record
Abstract
Security agencies in Canada have become increasingly anxious regarding the threat of domestic radicalization. Defined loosely as “the process of moving from moderate beliefs to extremist belief,” inter-agency security practices aim to categorize and surveil populations deemed at-risk of radicalization in Canada, particularly young Muslims. To detail surveillance efforts against domestic radicalization, this article uses the Access to Information Act (ATIA) to detail the work of Canada’s inter-agency Combating Violent Extremism Working Group (CVEWG). As a network of security governance actors across Canada, the CVEWG is comprised of almost 20 departments and agencies with broad areas of expertise (intelligence, defence, policing, border security, transportation, immigration, etc.). Contributing to critical security studies and scholarship on the sociology of surveillance, this article maps the contours and activities of the CVEWG and uses the ATIA to narrate the production and iteration of radicalization threats through Canadian security governance networks. Tracing the influence of other states – the U.S. and U.K., in particular – the article highlights how surveillance practices that target radicalization are disembedded from particular contexts and, instead, framed around abstractions of menacing Islam. By way of conclusion, it casts aspersions on the expansion of counter-terrorism resources towards combating violent extremism; raising questions about the dubious categories and motives in contemporary practices of the “war on terror.”
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.047 | 0.026 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".