The Military Revolution of Peter I – Quantitative Measurement
Bibliographic record
Abstract
Introduction. The article is devoted to the analysis of the processes related to the modernization of the Russian army in the times of Peter I. Owing to the magnitude and historical momentousness of these changes, we have introduced the term “revolution” in lieu of the term “reform” used hitherto in historiography. It is significant and noteworthy that these processes took place during the regular frontline military operations of the Great Northern War (1700–1721), when the tsarist army faced the perfectly organized Swedish army. Methods. So far, theories of military revolution and neo-institutional revolution have been deployed to show the transformations taking place at the time. Without denying the previous research findings, we have presented the modernization of the Russian army in the first quarter of the 18th century in quantitative terms. Hence, we have chosen three issues – recruitment, armament, and the number of officers in the army. Not only is there a sufficient source base for these issues, but they also allow for the time function in the ongoing transformations. Results. The figures under scrutiny indicate that the success of these military transformations was largely based on the recruitment system, which was superbly adapted in Russia. This made it possible not only to establish a regular national army of more than 100,000 soldiers, but also to maintain its headcount during the war despite the losses that the army suffered. In this way, almost half a million soldiers were recruited in Russia in the first quarter of the 18th century. The article emphasizes the conditions that had to be met to establish an army that could match the Swedish adversary. A key element was arming the military with modern firearms. Thanks to foreign purchases, primarily in the Netherlands, the rearmament was completed before 1709. The organizational structure of regiments, battalions, and rotas was also reorganized, so that the appropriate number of officers, non-commissioned officers, and military musicians was adjusted to the total number of soldiers. With the introduction of military discipline, it was possible to reduce the group of officers and musicians from 18.25% (1699/1700) to 10.15% (1711).
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".