Current tendencies of migration process development: global features and implications for Ukraine
Bibliographic record
Abstract
The article considers and summarizes the main global features and consequences of migration processes, including Ukraine. The purpose of the article is to establishing current trends in the development of migration processes, namely the global features and consequences for Ukraine. The grouping and generalization methods are used in the article (to represent the main effects of migration processes for donor countries, intermediate countries and recipient countries). The graphic method is applied to reflect the dynamics of changes in the number of emigrants from Ukraine, who were granted the first residence permits in the EU from 2009 to 2018. Methods of concretization and synthesis were used in determining the main consequences of migration processes for Ukraine. As a result of the research, the classification of world countries depending on the directions of migration flows (donor countries, countries of intermediate location and recipient countries) was determined. The list of the largest donor countries, recipient countries in the world with the indication of the number of migrants in these countries was determined. The main consequences of migration processes for world countries were determined, concretized and grouped according to the degree of their influence. The list of countries that are the largest centers of emigration for Ukrainian citizens (Poland, USA, Germany, Canada, Czech Republic) was determined. The main reasons for the increase in the number of emigrants from Ukraine in the periods from 2009 to 2012 and from 2012 to 2018 have been identified. The main consequences of migration processes for Ukraine, as a country-donor of human capital, a country of intermediate location and a recipient country, have been identified and grouped. The predominance of negative consequences of migration processes for Ukraine, as a donor country of human capital, a country of intermediate location, have been determined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".