An Accelerating Divergence? The Revisionist Model of World History and the Question of Eurasian Military Parity: Data from East Asia
Bibliographic record
Abstract
Over the past few years, this journal has hosted a debate central to world history and historical sociology: Joseph M. Bryant’s bold assault on the revisionist model of global history and the revisionists’ equally trenchant defense. A key point of disagreement concerns Europeans' relative military advantages vis-a-vis Asians. Both sides cite literature from historians’ Military Revolution Model, but each takes different lessons from that literature. The revisionists see a slight military imbalance in favor of Europe but deny that it reflects a general European technological lead. Bryant believes that the European technological lead is significant and reflects a more general modernizing trend. This article tries to resolve the disagreement by appealing to data from East Asia. First, it argues that recent work in Asian history points to what we can call a Chinese Military Revolution, which compels us to place the European Military Revolution in a larger, Eurasian context: not just western European but also East Asian societies were undergoing rapid military change and modernization during the gunpowder age. Second, it adduces evidence from a new study of the Sino-Dutch War of 1661-1668 (a war that both Bryant and the revisionists cite, each, again, taking divergent lessons) to come to a more precise evaluation of the military balance between China and western Europe in the early modern period: western cannons and muskets didn’t provide a discernible advantage, but western war ships and renaissance forts did. The article concludes that the revisionists are correct in their belief that Asian societies were undergoing rapid changes in military technology and practices along the lines of those taking place in western Europe and that the standard model Bryant defends is incorrect because it presumes that Asian societies are more stagnant than is warranted by the evidence. At the same time, the article argues that counter-revisionists like Bryant are correct in their belief that military modernization was proceeding more quickly in Europe than that in Asia, which may indicate that the counter-revisionists are correct on a basic point: there was an early divergence between the west and the rest of Eurasia. At first this divergence was slight – so slight, indeed, that it probably left little clear evidence in the noisy and poor early modern data we have available. But the divergence increased over time. Thus, we can speak of a small but accelerating divergence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".