The March to Militarism in Canada: Domesticating the Global Enemy in the Post-9/11, Neo-liberal Nation
Bibliographic record
Abstract
In Canada, as in other Western countries, much media and scholarly attention surrounding the “War on Terror” has focused on cases in which primarily Muslim citizens have been detained and charged under the anti-terror law passed hastily in 2001. However, little analysis has been conducted on how legislation and communicative strategies on terror have re-framed the domestic neo-liberal agenda, and, in so doing, fostered a militarized culture of surveillance and fear of the enemy inside its borders. This paper examines the Canadian government’s domestic politics of terror through its communication on the inter-related issues of crime, defence, security, and immigration that are propagated through the lens of the global War on Terror. Using parliamentary records, public documents and media stories, the paper suggests that, over time since it came to power in 2006, the government has invoked both the Muslim Other and a subtler, more generalized domestic enemy in order to capitalize on public concern and fear of terrorism to justify its neo-liberal legislative agenda and consolidate its power within the broader neo-liberal project. More than a decade after the events of 9/11, this case study and other research now exist on how the neo-liberal Canadian state uses fear and patriotism to achieve its domestic objectives through legislative and communicative strategies.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.045 | 0.016 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".