MILITARY CANTONISTS OF THE RUSSIAN ARMY IN THE FIRST QUARTER OF THE 19TH CENTURY
Bibliographic record
Abstract
The purpose of the article is an attempt to analyze the process of creating the institute of military cantonists of the Russian army, to trace the process of forming the legislative base and to establish their quantitative composition at the end of the first quarter of the 19th century. The main sources of the work were materials from the funds of the Russian State Military Historical Archive and a number of printed documents. The methodological basis of the study is the principles of scientific logic, objectivity, criticism and verification of sources. Among the basic methods of revealing the problem, one should mention the problem-chronological approach, historical-genetic and deductive methods, the method of analytical and comparative-historical typology. Scientific novelty of the work: for the first time in historiography, an attempt has been made to comprehensively analyze the reasons for the creation of the institute of cantonists of the Russian army, to highlight their legal status and calculate their number. Based on the analysis of the problem, the following conclusions can be drawn: various segments of the population of the empire were included in the category of military cantonists. Over time, they received a clear legislative basis. The establishment of this institution not only affected the size of the army reserve, the government had already begun to consider soldiers' children as one of the sources of replenishing the army with educated soldiers, called upon to finally eliminate conscription.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".