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Rural Settlement Network in Territory of Contemporary Omsk Region from Last Quarter of 19th Century to Early 20th Century

2023· article· en· W4390228026 on OpenAlexaboutno aff
Evgenia V. Sokolova

Bibliographic record

VenueNauchnyi Dialog · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
FundersRussian Science Foundation
KeywordsSettlement (finance)Rural settlementHuman settlementQuarter (Canadian coin)Arable landGeographyRural areaNatural resourceAgricultureNatural (archaeology)FrontierEconomic growthEconomyHistoryArchaeologyPolitical sciencePaymentBusiness

Abstract

fetched live from OpenAlex

The article examines the characteristics of the formation of the rural settlement network in the territory of modern Omsk region during the last quarter of the 19th century through the early 20th century. The relevance of this study is driven by societal and governmental interest in rural history, as well as by the expansion of the source base, which is related to the popularization of museum collections and personal archives. Throughout the research, the author analyzes a wide range of sources and identifies features of the territory’s settlement, lifestyle, and everyday life of peasants, as well as the nature of relationships between settlers and natives. The author concludes that natural and geographical conditions played a significant role in shaping the network of rural settlements, organizing economic life, and the daily routines of peasants. Particular attention is paid to the study of the economic life of peasants, as it was one of the defining factors in the formation of the rural settlement network of the territory. The investigation reveals that agriculture was the main occupation for peasants in the Omsk Irtysh region, despite the limited amount of arable land available. Livestock breeding was confined to its economic significance. Additional income for peasants depended on their settlement location and was determined by the natural resources of the territory. Natural and geographical conditions also dictated the external appearance of settlements.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

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 studies0.0020.001
Scholarly communication0.0010.001
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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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