Ships and Science: The Birth of Naval Architecture in the Scientific Revolution, 1600-1800
Bibliographic record
Abstract
s thesis, entitled 'Ships and Science'.In it I focused on the use of plan drawings in British naval architecture between 1580 and 1715, briefly arguing that scientific theory was of little use in early shipbuilding because it could not be used to make changes to the design drawings.I developed this argument more fully in my subsequent work having to do with stability theory in the 19th century, which I also made available to the author.It was, therefore, with considerable interest that I noted the title of this book.It was with considerable surprise that I read the preface, in which the author defines naval architecture as the application of scientific theory to ship design.This view is logically, historically, and historiographically mistaken.According to the dictionary, the term 'naval architecture' refers to both the design of ships and the superintendence of their construction.To equate naval architecture with theory alone is to confuse a small part with the whole.As for history, the phrase 'naval architecture' came into use in the late 16th century to describe a new approach to the design and construction of warships, organized around the use of measured, three-view, architectural-style drawings.Naval architecture was, in other words, already 'born' before this book begins.Originally, it had no connection to scientific theory whatsoever.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".