The securitisation of Muslims and the growth of far-right extremism in Canada
Bibliographic record
Abstract
This chapter explores how contemporary policies and discourses about the Canadian national security landscape have targeted and policed Muslims, such that they have become a ‘suspect community’ within the national imaginary. This has resulted in the securitising of Muslims in Canada. Though some may argue that the securitisation of Canadian Muslims is an outgrowth of anti-Muslim racism and bias that has come about from the 9/11 attacks and the subsequent War on Terror, this chapter traces the roots of these Islamophobic manifestations as part of broader historic practices associated with the racialised logics of coloniality. The securitisation of Muslims in Canada has manifested through surveillance and racial profiling as well as anti-terrorism legislation, which have normalised states of exception for Muslims with regards to their civil rights, as well as restrictions in movement of Muslims through a no-fly list. Ultimately, the hyper-securitisation of Muslims in the War on Terror draws attention away from other serious threats to Canadian society. These threats include the rapid growth of white supremacist and far-right extremist groups, which have targeted racialised and minority communities, including Muslims. This chapter explores the racialised logics underlying the securitisation of Canadian Muslims, the fatal consequences this has had for this suspect community, as well as the rapid growth of far-right extremist activism that has largely stayed under the radar in security discourses in Canada.
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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 | 0.034 | 0.011 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".