Bibliographic record
Abstract
The first comprehensive study of naval operations involving North American squadrons in Nova Scotia waters, Frigates and Foremasts offers a masterful analysis of the motives behind the deployment of Royal Navy vessels between 1745 and 1815, and the navy’s role on the Western Atlantic. Interweaving historical analysis with vivid descriptions of pivotal events from the first siege of Louisbourg in 1745 to the end of the wars with the United States and France in 1815, Julian Gwyn illuminates the complex story of competing interests among the Admiralty, Navy Board, sea officers, and government officials on both sides of the Atlantic. In a gripping narrative encompassing sea battles, impressments, and privateering, Gwyn brings to life key events and central figures. He examines the role of leadership and the lack of it, not only of seagoing heroes from Peter Warren to Philip Broke, but also of land-based officials, such as the various Halifax naval yard commissioners, whose important contributions are brought to light. Gwyn’s brilliant evocation of people and events, and the scholarship he brings to bear on the subject makes Frigates and Foremasts a uniquely authoritative history. Wonderfully readable, it will attract both the serious naval historian and the general reader interested in the ’why’ and ’what’ of naval history on North America’s eastern seaboard.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".