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Gypsies in Siberia (end of the 18th – 20th century)

2022· article· en· W4283715381 on OpenAlexaboutno aff
Vladimir N. Shaidurov, Natalia Sapronova, Yurii M. Goncharov, Tadeush A. Novogrodski

Bibliographic record

VenueJournal of the Belarusian State University History · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSoviet and Russian History
Canadian institutionsnot available
FundersRussian Foundation for Basic ResearchBelarusian Republican Foundation for Fundamental Research
KeywordsVagrancyQuarter (Canadian coin)PopulationHistoryGeographyEthnologyLate 19th centuryRelation (database)Period (music)Political scienceSociologyArchaeologyDemographyLaw

Abstract

fetched live from OpenAlex

The history of the Roma in Russia is a poorly studied topic. The article discusses the main stages in the formation and development of the Gypsy community in Siberia during the late 18th – 20th century. The authors came to the conclusion that the main source for the emergence and growth of the number of Roma in the region was migration, in which Belarusian Roma played an important role. On the basis of various sources, a description is given of the measures taken by the authorities in relation to the Roma population, aimed at its homogenisation and integration into the economic and socio-cultural processes in Siberia. However, all campaigns to combat Gypsy vagrancy in the 19th and 20th centuries did not lead to its complete eradication. The repressive steps both in the second quarter of the 19th century and in the 1930s did not help to solve the problem either. Only a part of the Gypsies switched from a traditional to a semi-sedentary way of life. Archival materials from central and regional archives. Most of the documents are introduced into scientific circulation for the first time.

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 score0.037
Threshold uncertainty score0.074

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)0.0030.001

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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