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Record W4394848045 · doi:10.17721/2518-1270.2024.73.13

Features of the Periodization and Classification of Ukrainian Emigration (Last Quarter of the XIX Century – 2023)

2024· article· en· W4394848045 on OpenAlexaboutno aff
Mykhailo Petryk

Bibliographic record

VenueEthnic History of European Nations · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor Market and Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationUkrainianIndependence (probability theory)PeriodizationQuarter (Canadian coin)Political scienceHistoryAncient historyLawMathematics

Abstract

fetched live from OpenAlex

The article analyzes the problem of periodization and classification of Ukrainian emigration in the last quarter of the 19th century – from September to October 2023. The topicality of the topic is that, despite the presence in the domestic historical science of a significant number of works devoted to various issues of Ukrainian emigration, at this stage there is no unified approach to its periodization, which especially concerns the definition of the chronological boundaries of the fourth waves and selection of the fifth as a separate complex stage of Ukrainian emigration in the period of independence. In addition, the question remains open as to how appropriate it will be to characterize the migration movement in the conditions of Russia’s full-scale war against Ukraine as the beginning of the sixth wave of Ukrainian emigration. In the course of the study, on the basis of available scientific publications on the topic and the memories of the emigrants themselves, the reasons for each wave of Ukrainian emigration were analyzed and how they differed at different stages, which, accordingly, led to the beginning of a new period of mass migration. The prerequisites of Ukrainian emigration before and after the restoration of independence were also compared. The author also emphasized the diversity and ambiguity of the reasons for the migration movement after 1991. The peculiarities of each wave of Ukrainian emigration and their separate main periods were determined according to such criteria as motives, character, duration, distance, volume, level of education, organization, goals and purpose, etc. The author also focused attention on how much the vector of the migration movement changed as a result of the Russian armed aggression against Ukraine, which began in the spring of 2014. A special emphasis was placed on the impact of such a challenge as Covid-19 on Ukrainian emigration. The author also identified the perspective of further research, which consists in comparing the peculiarities of the periodization and classification of Ukrainian emigration with the peculiarities of the migration processes of another European country in the specified period.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.191

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)0.0000.000

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 head0.032
GPT teacher head0.225
Teacher spread0.194 · 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 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
Published2024
Admission routes1
Has abstractyes

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