Bibliographic record
Abstract
The subject of this study is the cavalry schools of the second quarter of the 19th century as an independent part of Russian special military education. It is at the intersection of several relevant areas for modern National history. This is the history of the Russian Imperial army, Russian military education and the Military Reforms of Nicholas I. The objective of the study is to analyze of development dynamics of the Russian cavalry schools in the reign of Nicholas I. The research tasks: to reconstruct the history of special military education in Russian cavalry in 1801-1825 and to reveal its key moments. The sources for the study are military and administrative documents of the second quarter of the 19th century and the “annals” of Russian military schools. The methodological basis of the study is the chronological and problem method. The emphasis is the formation process of the leading St. Petersburg schools, as well as on the state of the matter on the periphery of the Russian Empire. We characterized Russian cavalry education of all levels: from canonist’s recruiting schools and preliminary horse riding in non-core cadet corps to highly specialized courses in the Guards Riding-master School and the School of Guards Sub-lieutenant and Cavalry Junkers. The Imperial Military Academy is the highest level of education. This research allowed identifying and characterizing the development dynamics of Russian cavalry education in the second quarter of the 19th century. We conclude that during this period it received a unified system and a considered hierarchical structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".