CHANGES IN MIGRATION PREFERENCES OF UKRAINIANS DURING THE RUSSO-UKRAINIAN WAR
Why this work is in the frame
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Bibliographic record
Abstract
Problem Description and Purpose of the Study. Due to Ukrainian forced migration, the population decreased by almost a third. Moreover, the migration process continues actively. The task of the study is to analyze the population’s readiness for migration and preferences regarding the choice of countries. Problem Statement and Purpose. The study analyzed the dynamics of Ukrainians’ willingness to move abroad, the main factors that influenced such a decision, and the establishment of regional specifics regarding the choice of countries for migration. Data & Methods. The study uses UNHCR data on the number of refugees. The primary analysis tool is Google Trends – a free tool from Google that provides information about the popularity of specific search queries on Google during a certain period and in certain regions. Based on the analysis of requests, peaks, and dynamics of requests for migration, as well as interest in migration to different countries, were established. Results. According to the study results, apparent regional differences were established in the migration attitudes and preferences of the Ukrainian population. Western regions remain the leaders in search queries, which is explained by the concentration of IDPs and the traditional labor migration of the population of these regions. The peak of search queries came at the beginning of summer. The formation of the second wave of migration explains this. The number of Russian-language requests, and even more requests regarding departure to Russia, is a cause for concern. If this is almost the norm for the occupied regions, then for those remote from the front and the capital, it is a task for the national security structures. Peaks of requests in certain regions come either after large-scale missile attacks, before the winter of 2022, or before the start of specific migration programs or increased payments to refugees in certain countries. Regarding the choice of countries for migration, the intensity of requests almost corresponds to the number of Ukrainian refugees in the respective countries. The leaders are Germany, Poland, the Czech Republic, Great Britain, the USA, and Canada. Exceptions are only certain neighboring countries with a stable ethnic or labor connection. The grouping of regions of Ukraine according to migration attitudes and population preferences formed separate regions. The first type includes the partially occupied regions of the south and east, with low indicators of migration attitudes and a predominance of Russian-language requests. The second group included as many as 11 regions and Kyiv. This type is characterized by average indicators of migration attitudes and a relatively wide variety of choices of countries for migration. However, preference is given to the USA, Great Britain, and Canada. The second and third types of regions have similar characteristics. As one moves to the western borders, the interest in migration increases. Regarding the choice of countries for migration, European countries dominate more.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it