Michael J. Taylor. <i>Soldiers and Silver: Mobilizing Resources in the Age of Roman Conquest</i>.
Bibliographic record
Abstract
Historians have asked how Republican Rome acquired its Mediterranean empire since the period itself, when the Greek historian Polybius posed the question of Rome’s rise as a topic all serious intellectuals needed to consider. In this tightly written monograph, based on a 2015 dissertation, Michael Taylor offers an answer: resource differentials. In his earlier work, Taylor has produced a string of excellent articles on various aspects of Roman Republican military history, so unsurprisingly for him the topic of resources comes down to how many soldiers Romans and their adversaries recruited and how they financed that manpower. Taylor argues that Romans won ultimately because they were able to muster a larger fighting force. The narrative includes a number of fresh and more nuanced thoughts about how Romans paid for and deployed their numerical advantage. The introduction and conclusion gesture to Michael Mann’s theory of infrastructural power, but the granular exposition of ancient manpower and state budgets that makes up the bulk of the volume will appeal above all to specialist readers. What makes Taylor’s work original is his juxtaposition of tallies for Roman resources beside those of Rome’s opponents—namely, Carthage and the Hellenistic kingdoms. The core of the book falls neatly into two parts, each dividing into chapters on the manpower and then finances of Rome and its rivals. The time frame moves from the war against Pyrrhus through the Third Macedonian War (ca. 280–168 BCE). For Roman resources, Taylor relies largely on figures found in extant narratives of Polybius and Livy. For Rome’s rivals, the approach varies according to more heterogeneous source materials. This difference is most noticeable with regard to Carthage, in which discussion perforce relies heavily on Roman authors, with all the rhetorical problems such a perspective implies. But Taylor is duly cautious and provisional where need be, and he ultimately creates a detailed and sound basis for comparison.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".