Crisis phenomena in Central Asian direction development of Russian foreign trade in the Orenburg direction in the 1st quarter of the 19th century
Bibliographic record
Abstract
The paper contains analysis of development tendencies of the Russian Empire foreign trade with Central Asian khanates in the first quarter of the 19th century. The authors found that the Russian State didnt pay much attention to the Asian customs policy in this direction for a long time. It was due to the fact that the trade with Central Asian khanates was of exchange and caravan character. The author came to the conclusion that the heads of the Orenburg Region - military and civil governors - made great efforts to change that situation and made special rules for the foreign trade development in the Orenburg Region. It promoted commodity turnover increase. The author proved that in the first quarter of the 19th century the most important element of Central Asian trade development crisis in the Orenburg direction was the fact that merchants from Central Asia dominated Russian merchants in the numerical ratio. However, the ministry of finance and E.F. Kankrin refused to forbid Central Asian merchants to trade at internal Russian fairs as it would result in stagnation in trade and would make prices for goods higher. This problem for the first quarter of the 19th century couldnt be solved as it was connected with the geopolitical status quo existing in the region. It only started to get solutions with an active military advance of Russia to Central Asia in the second half of the 19th century.
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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 | 0.001 | 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) | 0.002 | 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".