Development alternatives for arms production in Russia during the reign of Peter the Great (the late 17th - first quarter of the 18th centuries) as reflected in Russian historiography
Bibliographic record
Abstract
This paper looks at how the production infrastructure for the firearms branch of the military industry emerged in Russia during the reign of Peter the Great (1689-1725). The objective of the study is to identify alternative development patterns for this industry, both in terms of methods of organizing arms production and its geographical location. In the study, the author referred to the works of Russian researchers from the pre-revolutionary, Soviet, and postSoviet periods; their historiographic analysis makes it possible to reach the research objective. Aside from the traditional methods like analysis and rising from the concrete to the abstract, common for philosophical studies, the author also applied research methods specific to history: problem chronology and prospective analysis. The research demonstrates that during Peter the Great's reign, the development of the firearms supply system for the armed forces was influenced by two trends, which took two different directions: one was the encouragement of private initiative; the other was the development of a state-funded network of arms enterprises and the centralization of the arms business in the arms makers' community (sloboda) in Tula. Ultimately, the latter trend prevailed, and the government focused its efforts on expanding the geography of arms production and searching for the most optimal location where arms enterprises could be deployed. The study documents the fate of all potential arms production hubs that emerged during Peter the Great's reign. Some of them never left the initial proposal stage (Yaroslavl, Ustyuzhna Zheleznopolskaya, ore processing plants in the Urals). Others did play a certain role in supplying the Russian army with firearms during the challenging period of the Great Northern War but gradually ceased their operations when the war was over (the Olonets plants (Lipetsk and Petrovsky plants), the arms enterprise in Tobolsk). Moscow, which used to be an important firearms production hub before Peter rose to the throne, lost this status under the new ruler, just as its status as Russia's capital. The new capital, St. Petersburg, was never even intended to be an arms hub. In the author's opinion, the government policy on building up Russia's arms industry prioritized the development of the long-established production assets in Tula (Tula Arms Plant) and the construction of a new arms plant in Sestroretsk, northwest Russia, within immediate reach of the Great Northern War's front lines. These state enterprises, along with the Izhevsk Arms Plant, which was built in the early 19th century, became the key suppliers of firearms for the Russian army and navy throughout the entire imperial period.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".