The reasons for the creation of military settlements of cavalry in the Russian Empire in the first quarter of the XIX century
Bibliographic record
Abstract
The history of military settlements in the Russian empire of the XIX century in recent decades has been actively explored, however, until recently there were almost no works devoted to the reasons for the establishment of military settlements. Therefore, the purpose of the article is to analyze the reasons for the organization of emperor Alexander I military settlements of the cavalry in the south of the Russian empire in the first quarter of the XIX century. The research methodology was chosen according to the purpose and was based on the principle of historicism. It is represented mainly by general scientific methods (analysis, deduction, induction) and comparative-historical method. The scientific novelty is that, based on the analysis of archival sources and scientific literature, the circumstances of the creation of military settlements of the cavalry in southern Russia are analyzed. Conclusions. In the first place, the idea of the introduction of military settlements arose in connection with the difficulties of recruiting an ever-expanding army. Existing for more than a century, the recruitment system of the army due to the recruitment of the population was no longer able to meet the needs of the army in human resources. Another reason was an attempt to resolve financial problems. More than half of the state budget was spent on military needs. Given these reasons, the government of Alexander I decided to transfer part of the troops to the system of self-sufficiency of food and feed. In 1817, the organization of settlements of cavalry districts in Ukraine began. It was decided to settle the cavalry in the Sloboda-Ukrainian, Kherson and Katerinoslav provinces. Starting the establishment of military settlements, the government of emperor Alexander I pursued political, socio-economic and military-strategic goals. And if it were possible to successfully implement the conceived in practice, then government spending on the maintenance of a large army would be considerably reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".