Global Order, US Hegemony and Military Integration: The Canadian-American Defense Relationship
Bibliographic record
Abstract
This article argues that the contemporary IR literature on global order and American hegemony has limitations. First, the critical discourse on hegemony fails to adequately examine the deeply embedded nature of regularized practices that are often a key component of the acceptance of certain state and social behaviours as natural. Second, much of the (neo)Gramscian literature has given primacy to the economic aspects of hegemonic order at the expense of examining global military/security relations. Lastly, much of the literature on global order and hegemony has failed to fully immerse itself within a detailed research program. This article presents an historical sociology of Canada-US defense relations so as to argue that the integrated nature of this relationship is key to understanding Canada's role in American hegemony, and how authoritative narratives and practices of “military integration” become instrumental and persuasive in establishing a “commonsensical” worldview. The effects of such integration are especially clear in times of perceived international crisis. Our historical analysis covers Canada's role during the Cuban missile crisis, Operation Apollo after 9/11, and the current war in Afghanistan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".