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“RUSSIAN CENTAURS”: CAVALRY SCHOOLS IN THE REIGN OF NICHOLAS I

2018· article· en· W2903672412 on OpenAlexaboutno aff
Bella L. Shapiro

Bibliographic record

VenueHistorical and social-educational ideas · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicRousseau and Enlightenment Thought
Canadian institutionsnot available
Fundersnot available
KeywordsReignQuarter (Canadian coin)CadetEmpireAnnalsState (computer science)Russian historyFatherlandAncient historyHistoryLawClassicsPolitical sciencePoliticsArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.255
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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