Economic Development of Siberia during Reforms of Peter I: Craft and Emergence of Industry
Bibliographic record
Abstract
The features of the industrial development of the Siberian region at the end of the 17th — the first quarter of the 18th centuries are studied. The analysis of legislative and clerical, statistical sources, generalization and systematization of the results of research in the field of development of the Siberian handicraft and manufacturing production of the late 17th — first quarter of the 18th centuries made it possible to identify the degree of influence of Peter I’s reforms on this process. It is concluded that in the period under review, the main form of industrial production in Siberia was small-scale handicraft production, in many sectors of which progress was observed, due both to the improvement of the technological process and to the regulatory orders of Peter I of a national and regional nature. It is noted that in the complex of transformative undertakings, Peter I paid special attention to Siberia, although in some cases the regulatory provisions of the royal decrees regarding certain types of handicraft production did not give much effect in the region, as was the case, for example, with the decrees of 1715 on the prohibition of tanning leather without the use of blubber and the manufacture of narrow linen and hemp cloths. The most significant government decrees with a regional focus on crafts related to stone building and river shipbuilding. The authors of the article show that an important result of the reform activities of Peter I was the development of large-scale manufactory and factory production in Siberia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".