The Heirs of Archimedes: Science and the Art of War through the Age of Enlightenment
Bibliographic record
Abstract
and Tamera Dorland's edited volume grew from presentations made at two conferences, 'Science and Warfare in the Old Regime ' in 1998 and 'Colonels and Quartermasters' in 1999.The editors, by collecting these works, are trying to correct what they see as a lack of attention paid to the historical relationship between military technology and science; what attention it has received places it in the 20th, possibly the 19th century, whereas Steele and Dorland see this relationship emerging much earlier, with a significant flowering after the Renaissance.They introduce the collection by claiming, as did Voltaire, that this relationship extends as far back as the time of Archimedes, and then they shed light on that classical beginning.Through the succeeding essays, they hope to answer the question 'In short, when did Archimedes have real intellectual heirs who re-created for themselves his personal union of science and the art of war?' [3].That brings up a key point of this book, particularly in regard to its title: despite the introduction, the focus of the book is firmly on the 'Heirs' and not on 'Archimedes'.The introduction gives an excellent, concise, historical, and historiographical account of the science-military connection.I found that some parts of this introduction, though, lead the reader down the wrong path by creating some expectations of discussions that never occur in the collected essays (nor should they, I have also come to believe).In describing Archimedes' intermingling of science and war, the editors provide a framework, accompanied by a diagram, that maps connections, decisions, and/or movements made by Archimedes.Steele and Dorland draw on modern terminology to write sentences such as 'For Archimedes, the science of mechanics may have
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".