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The long way home: Migration trends of Ukrainian researchers in the modern world (1991‒2023)

2023· article· en· W4390167888 on OpenAlexaboutno aff
Tetiana Karmadonova

Bibliographic record

VenueHistory of science and technology · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor Market and Education
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianPoliticsAnnexationContext (archaeology)Political sciencePeriod (music)Sociocultural evolutionGeographyEconomic growthDevelopment economicsLaw

Abstract

fetched live from OpenAlex

In a modern world marked by intense migration processes, the analysis of the migration of Ukrainian scientists and their choice of destination countries, especially in the context of recent events in Ukraine, is an important subject of scientific research. This study examines migration trends among Ukrainian researchers in various historical periods from the early 1990s to the present. The research is based on the analysis of scientific literature for theoretical insights and previous studies, the use of statistical data from the State Statistics Service of Ukraine, the analysis of political, economic, and sociocultural contexts to understand migration factors, methods of observing real events and processes, and historical analysis to trace the evolution of migration processes. Factors influencing scientists' decisions regarding migration and their motivations, as well as destination countries, were analyzed in the article. Three key migration stages are highlighted: the post-Soviet period (1991–2012), the post-revolutionary period (2013–2021), and the period of full-scale war (2022 and onwards). The first stage, covering the years 1991–2012, was characterized by the outflow of scientists in search of economic opportunities and stability. Destination countries during this stage included the USA, Russia, Germany, Israel, Canada, and Poland. The second stage, from 2013 to 2021, was marked by deep social and political transformations in Ukraine following the Euromaidan Revolution and the annexation of Crimea by Russia. Scientists chose Germany, Canada, and Poland for academic collaboration and research funding.The third stage, which began in 2022 and continues to the present, is defined by the full-scale war in Ukraine. Scientists are leaving the country due to a sense of danger and military conflict. The primary migration destinations are EU countries, which offer opportunities for academic cooperation and safety. Prospects for further scientific research lie in the analysis of the historical roots of the migration of Ukrainian scientists, including the impact of events and reforms in Ukraine and the world on migration processes.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.074
GPT teacher head0.267
Teacher spread0.193 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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