On the Question of the Creation and the Beginning of the Functioning of the Military School of Russia at the End of the XVII — First Quarter of the XVIII Century
Bibliographic record
Abstract
The history of military educational institutions dates back to the time of Peter the Great and is an important page in the social history of the origin and development of education in Russia. Artillery and engineering military educational institutions and the School of Mathematical and Navigation Sciences were among the first special educational institutions that performed an important state task of training command personnel and specialists in the field of naval, artillery and engineering. The article traces the history of the creation and subsequent development during the first quarter of the XVIII century of individual elements of the national system of military personnel training. The already known individual information and data concerning military educational institutions that functioned in the XVIII century are supplemented and expanded by poorly studied materials and archival sources that have not been introduced into scientific circulation. Attention is focused on certain aspects of the organization of the educational process and daily life in them. The author concludes that despite all the difficulties that the state and military administration had to face when establishing and launching the first military special schools, their further functioning in the first quarter of the XVIII century marked a new stage in the development of domestic military special education and enlightenment. Previously unpublished sources are introduced into scientific circulation.
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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 | 0.002 | 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 | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".