Warming Up to the Cold War: Canada and the United States' Coalition of the Willing, from Hiroshima to Korea
Bibliographic record
Abstract
When U.S. President Harry Truman asked his allies for military support in the Korean War, Canada's government, led by Prime Minister Louis St-Laurent, was reluctant. St-Laurent's government was forced to change its position however, when the Canadian populace, conditioned to significant degrees by the powerful influence of American media and culture, demanded a more vigorous response. Warming up to the Cold War shows how American cultural influence helped to undermine waning Canadian nationalism. Comparing Canadian and American responses to events such as the atomic bomb, the Gouzenko Affair, the creation of NATO, and the Korean War, Robert Teigrob traces the role that culture and public opinion played in shaping responses to international affairs. With penetrating political and cultural insight, he examines the Cold War consensus between the two countries to reveal the ways that Canada cited .home-grown. rationales to justify its increasing subservience to American strategy and posturing. Full of fascinating insights, Warming up the Cold War is essential reading for anyone interested in the Cold War, the role of culture in politics, and the history of U.S.-Canada relations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".