The Genealogy of National Security Discourse and Intelligence Practices in Canada: Law, Racism and Racialization
Bibliographic record
Abstract
Conventional approaches to security analysis emphasize racialized national security practices within the post 9/11 context, neglecting the genealogical dimension that offers a unique perspective on Canada’s current anti-terrorism laws and intelligence practices. This thesis employs Michel Foucault’s method of genealogy to explore the present history of the Canadian national security discourse, focusing on how the law facilitates the construction and (re)production of racism through security intelligence practices. Genealogy is a critical method that enables a joint philosophical and historical examination of present problems, also referred to as problematization. Canada’s national security discourse has a particular genealogy dating back to the nineteenth century. Most importantly, this study investigates the construction of race within the national security discourse to shed new light on racism and racialization while exploring the legal developments to analyze the gradual shifts in intelligence practices. The research’s central focus is to explore historical events to establish the present link between security and race in Canada. It emphasizes the relationship between the law and the exercise of power through discursive national security and intelligence practices, racializing minority populations such as Arabs and Muslims. A genealogical inquiry is necessary to provide a comprehensive account of continuities, ruptures, and characteristics of security and intelligence practices, thereby addressing a critical lacuna in the existing literature. It also contributes new knowledge to a poorly conceptualized global security problem, which serves as a basis for law and policy reform in Canada and internationally.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.044 | 0.023 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".