Surveillance Strategies and Populations at Risk: Biopolitical Governance in Canada’s National Security Policy
Bibliographic record
Abstract
Abstract This article examines how Canada’s new national security policy operates through language and practices that take elusive risks to the health and safety of the population as an opportunity for action, and is made possible through an expansion of surveillance. The biopolitical character of security has greatly reduced the traditional distinction between the state as a military apparatus and the state as a service provider and manager of the citizenry. The article argues that the biopolitical governance strategies of Canada’s national security policy treat the problems for political freedom, equality and democratic accountability posed by encroaching security measures as largely negligible in the face of indeterminable danger. Using a Foucauldian analysis, the article establishes the connection between biopolitics and security. It subsequently examines how the Canadian policy deploys truth claims about the immanence of ‘threat’ and how claims about Canadian values produce an internal ‘other’ that represents the proliferation of threats. The article then focuses on two principle techniques of governance: first, guarding the freedom, health and safety of the population, and, second, expanding surveillance to give national security a totalizing reach. The article concludes by theorizing the implications of security governance for legitimating racial profiling and the ‘war on terror’.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".