Racialized knowledges: understanding the construction of the Muslim ‘terrorist’ in the policy process
Bibliographic record
Abstract
This paper highlights the mobilization of racialized administrative power and racial knowledge in counterterrorism policymaking. We apply Critical Discourse Analysis to Canadian parliamentary debates in two significant policy periods corresponding to acts considered ‘terrorism’: passage of Bill C-51 following acts of violence by Muslims, and Motion 103 (M-103) recognizing Islamophobia following the 2017 racially motivated mosque massacre (of Muslims). We show how the threat of ‘terrorism’ is constructed and the discursive strategies used to legitimize extant counterterrorism knowledge. Building on critical policy approaches and critical terrorism studies, we highlight 1) racialization of Muslims through the ‘terrorism’ discourse; 2) presentation of racialized counterterrorism as color-blind; and 3) reframing concerns about systemic Islamophobia to uphold the (racial) status quo. We show that regardless of political party and who commits acts deemed ‘terrorism’, state security institutions maintain the association of ‘terrorism’ with Muslims. Policymakers rely on white logic to depict state institutions as neutral, obscuring their inherent anti-Muslim orientation. Concerns about systemic Islamophobia are addressed through incremental reforms, keeping intact the racial knowledge underpinning these institutions. Our research suggests tackling race holistically in policy studies requires greater focus on institutions and structures that produce and disseminate racialized policy knowledge.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.029 | 0.111 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| 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".