Villages of the Black Earth Region of European Russia in the 17th – First Third of the 18th Centuries
Bibliographic record
Abstract
Introduction. In the 17th century, in the south of European Russia, in the Black Earth districts, the process of economic development of vast fertile lands took place. The effectiveness of this process was associated with the increase in the number of Russian settlers to work on arable land. Economic success played an important role in the confrontation between Russia and the Crimean Khanate. The war between the two states lasted throughout the 17th century. In recent decades, the study of the history of the development of black earth districts in Russia has continued in various directions, but the problem of economic development of new territories remains relevant for study. Methods and materials. The authors of the article use data on the number of villages as a criterion for assessing the process of reclamation of empty lands. The authors focus on the villages of the three largest regions of the Black Earth region – the Belgorod, Voronezh, and Yelets regions. Materials from the Kozlov region are used as additional information. The materials for the work were mass sources – census books of the 17th – first quarter of the 18th centuries. Results. As a result of the study, an increase in the number of rural settlements and changes in their internal structure were recorded in favor of the increase in large villages. All this testifies to Russia’s economic success in the region and allows us to understand some of the features of this process. There is also no doubt about the steady demographic growth throughout the 17th century. These factors played an important role in the struggle for the annexation of the Black Earth Strip to Russia during periodically escalating military conflicts and political confrontations. Contribution of the authors of the article: D.A. Lyapin – concept development, writing of the introduction, analysis of the results. A.R. Melnikova – processing of materials for the article, writing of the section “Materials and methods,” design of the article. D.A. Lyapin – 50%, A.R. Melnikova – 50%. Funding. The research was carried out at the expense of a grant from the Russian Science Foundation (project No. 24-68-00011, https://rscf.ru/project/24-68-00011/) on the basis of Bunin Yelets State University.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".