Bibliographic record
Abstract
The Royal Canadian Navy (RCN) is well-known for its specialisation in antisubmarine warfare (ASW). From its trial-by-fire during the Second World War to the present-day participation in numerous international ASW exercises, much of the literature on the RCN&s;s military tasks focuses on the development of its ability to defend and deter submarine attacks on trans-Atlantic shipping. During the Cold War, the acquisition and maintenance of this dedicated ASW capability came at the expense of nearly every other naval warfare mission. However, the RCN&s;s focus on ASW was not a foregone conclusion, and indeed its actual combat missions since the end of the Second World War have rarely required that specialised capability. Such was the case with the RCN&s;s participation in the Korean War. Amidst a wholesale transformation of its fleet force structure towards ASW in the North Atlantic, the RCN was called away to the opposite side of the world. Not only was this deployment carried out initially by the Pacific fleet that would soon be relegated to a mere ‘training’ force, but the mission itself also involved nearly none of the ASW specialisation that was gained during the last war and was being cultivated in new construction. This chapter contextualises the RCN&s;s participation in the Korean War within its overall postwar transformation towards a dedicated Atlantic antisubmarine force. It highlights the challenges inherent in a smaller-sized navy&s;s attempt to match naval means with strategic ends during times of peace and the importance of multi-mission capabilities for countries with transoceanic interests.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.355 | 0.166 |
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".