Bibliographic record
Abstract
<JATS1:p>The study of war in all periods of prehistory and recorded history has always commanded the attention of historians, dramatists, poets and artists. The study of peace has, however, not yet gained a comparable readership, and the subject is attracting an increasing amount of scholarly research.</JATS1:p> <JATS1:p>This volume presents the first work of academic research to tackle this imbalance head on. It looks at war and peace through the ages, from the Classical world through to the eighteenth century. It considers the nature and advocacy of war and peace both from an historical perspective but also a philosophical one, particularly looking at how universal peace, which began as a personal philosophy, became over the centuries a political philosophy that underpins much of modern society’s attitudes towards warfare and militarism.</JATS1:p> <JATS1:p>Roger Manning begins his journey through history by looking at the Greek martial ethos and philosophical concepts of peace and war in the ancient world; moving through the Roman empire’s military advances, he explores the concepts of war and peace in the medieval world and the Renaissance, with the writing of Machiavelli and Erasmus; finally, his account of the search for a science of peace in the seventeenth and eighteenth centuries brings the book to its conclusion.</JATS1:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".