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Record W2972966462 · doi:10.31518/2618-9100-2019-4-9

Evacuation of civilians in the Central Asian republics of the USSR during the Great Patriotic War

2019· article· en· W2972966462 on OpenAlexaboutno aff
M. P. Belenko

Bibliographic record

VenueHistorical Courier · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsKazakhUzbekPopulationQuarter (Canadian coin)GeographyCentral asiaAccommodationWorld War IISpanish Civil WarPolitical scienceEconomic historyDemographyEconomic growthAncient historySocioeconomicsHistoryEconomySociologyArchaeologyPsychologyEconomics

Abstract

fetched live from OpenAlex

Central Asia, along with the Urals and Western Siberia during the Great Patriotic War was one of the key rear regions of the USSR, and took a significant number of evacuated citizens.This article is devoted to the issues of the number of migrants, their transportation routes and features of accommodation in the region.The study of the existing statistical data showed that the errors of population accounting in 1941-1942 were large and could reach 15-20 % of the total number of people who arrived to evacuate the population.In addition, the population registers, for various reasons, provided the authorities with information on the number of evacuees with a great delay.However, it can be argued that all the years of the war, Central Asia has taken more than 800 thousand people, three quarters of which arrived in the region in 1941, and one quarter in 1942 the Largest number of migrants adopted by the Kazakh and Uzbek SSR -a total of about 80 % of the total number of evacuees.The reasons for this distribution were the high pre-war demographic and infrastructural potential of these Union republics.

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)0.001
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 score0.100
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.002
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 head0.014
GPT teacher head0.234
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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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