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Development of the Upper Don region in the first quarter of the XVII century: rural settlements of Yelets and Voronezh counties in 1615

2024· article· en· W4399727196 on OpenAlexaboutno aff
K. G. Gaiterova

Bibliographic record

VenueHistory Facts and Symbols · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Human settlementRural settlementGeographyHistoryAncient historyArchaeologyRural areaPolitical science

Abstract

fetched live from OpenAlex

Introduction. The article is devoted to rural settlements of the Upper Don region in the first quarter of the XVII century. The geographical features of the foundation of the first rural settlements of Yelets and Voronezh counties are considered, as well as their social composition is presented. Methods and materials. The methodological basis of the research is represented by the fundamental principles of historical cognition, which is used in Russian historical science in the study of socio-economic processes of society and its structural components: historicism, objectivity and consistency. General scientific, comparative-historical, typological, structural-functional and probabilistic-statistical methods, as well as the principle of objectivity, are used to consider issues related to the development of rural settlements of the counties under consideration and their social composition. The fundamental source for our research was the Watch Books on the Yelets and Voronezh counties of 1615. Results. The author of the study concludes that despite the adjacent location of the territories under consideration, it cannot be said that their development was identical. The characteristic feature of the "cluster" principle of settlement, which became widespread in the Yelets district, was extremely poorly expressed in Voronezh. However, it is also necessary to note a number of common features, primarily related to the predominance of the largest administrative units, such as villages and villages, compared with smaller repairs and wastelands. In addition, the social composition of the first settlements was approximately homogeneous in its composition and included the main categories of the population, such as landowners with and without peasants, clergymen, service people and patrimony. Conclusion. The main distinguishing feature of the social composition of the first rural settlements was the small number of peasants. This was due to the fact that the first settlers practically did not have their own peasants, since they were still quite young and did not own land and estates.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.240
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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