ENTREPRENEURSHIP ACTIVITY IN THE URALS IN THE 18 CENTURY (ON THE EXAMPLE OF THE MINE OWNERS OSOKINS)
Bibliographic record
Abstract
This article addresses the issue of entrepreneurship on the example of Osokins. Scientific novelty of research consists in the fact that for the first time in Russian historiography provided by the mining activities of the merchants Osokins. Of the large industrial complexes in the Urals, owned by merchants, the mining industry of the Osokins was formed earlier than all others. The ancestors of this family came from the monastic peasants of the Balakhna County of the Kazan province. The source of the initial accumulation of capital from the Osokins was grain trade and grain contracts. Cousins Petr Ignatievich and Gavriil Poluyektovich started their mining business in the Urals in 1729 with the construction of a joint Irginsky plant. If we proceed from the number of factories, the industrial estate of I.P. Osokin was the largest in Russia, since none of the industrialists of Russia at the end of the 18th century. However, according to the production capacity I.P. Osokin was inferior to the industrial complexes of many large factory owners. It is important to note that the Osokin farm during the last quarter of 18 century slowly rolled down. In the 1800s. almost all the Osokins’ factories were taken over by the merchant A.A Knauf. The author concludes that entrepreneurial activity of the Osokins in the field of mining industry is a positive example of how people from ordinary peasants could express themselves in this area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".