CHANGES IN MIGRATION PREFERENCES OF UKRAINIANS DURING THE RUSSO-UKRAINIAN WAR
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».