THE HISTORICAL CONDITIONS OF DEVELOPMENT OF THE MINING INDUSTRY IN THE URALS IN THE XVIII CENTURY
Bibliographic record
Abstract
This article is devoted to defining the historical conditions for the development of the mining industry in the Urals in the 18 century. The author dwells on the problem with land of mountain plants. The formation of the mining industry in the Urals had many challenges. One of the essential task was to ensure the mining plants to the ground. Describing the problem, the author assesses the actions of the local population and the Imperial government. Construction of mining factories, fortresses and colonization of this territory migrant population was accompanied by the seizure of Bashkir large part of their lands and increase of various duties. Therefore, Bashkirs strongly opposed the intensification of the policy of the government in the province. The article discusses the issue of ensuring the plants artisans and working people. The government assisted entrepreneurs in providing them with qualified personnel, craftsmen. On the construction and start up in action factories of the southern Urals artisans were sent to people from the old factories of the Middle Urals for a permanent job or temporary - to train local craftsmen. The main supplier of qualified personnel for the South Ural factories were Ekaterinburg state-owned factories. The author concludes that since the first quarter of the 18 century the mining segment of the Ural industry is becoming a leading and strategically important for the whole Russian Empire, modernization processes in metallurgy and related industries acquire primary importance in the economic modernization of the Urals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".