The Chiefs of Staff Committee and Continental Air Defence, 1954–60: How Not to Manage Defence Policy
Bibliographic record
Abstract
The place of the military in the development of defence policy has long been a problem to Canada’s central organization for defence. While the primacy of civilian politicians has never been in doubt, the organization of military input to decision-making has been revised several times and remains in question. In 1947, Defence Minister Brooke Claxton reorganized the Canadian central organization for defence by creating the Chiefs of Staff Committee (csc), appointing a permanent chairman, Lieutenant-General Charles Foulkes, in 1951. While some historians have considered the csc’s “Command Age” to have been a golden one, recent analyses have been more critical but have not assessed the csc as an institution. This article looks in depth at the csc’s management of continental air defence during the 1950s and 1960s – Canada’s single most important defence project. It argues that the csc failed to provide objective advice to ministers. Under Foulkes, it carefully edited analyses submitted to Cabinet, manipulated reviews, and suppressed dissent in order to support its preferred manned interceptor option. As a result, the ministers and the senior public service lost confidence in the armed services, setting the stage for a massive reorganization in the 1960s that was aimed at strengthening the role of civilians in defence policy.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".