âNecessary Stepping Stonesâ: The Transfer of <em>Aurora</em>, <em>Patriot</em>, and <em>Patrician</em> to the Royal Canadian Navy after the First World War
Bibliographic record
Abstract
Canadians seem to have difficulty in understanding the importance of naval forces in the defence of their nation. Twice in the early years Canada took the first steps towards the creation of a useful fleet, but then lost interest. The acquisition of Niobe and Rainbow for training the nascent Royal Canadian Navy (RCN) in 1910 was a good beginning, but even before the First World War broke out in 1914, the government’s priorities changed. A second, more promising start was made immediately after the end of hostilities. In the spring of 1919, hesitant discussions began which led to the commissioning of His Majesty’s Canadian Ships Aurora, Patriot and Patrician in November 1920. But why were these particular three chosen, and were they of any value?
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".