THE IMPACT OF WAR-INDUCED FORCED MIGRATION ABROAD ON THE STATE OF UKRAINE'S LABOR POTENTIAL IN THE CONTEXT OF SOCIAL SUSTAINABILITY
Bibliographic record
Abstract
The labor potential is an important component of the country's economic potential, and its quan-titative and qualitative characteristics significantly affect the rate of economic growth and social sus-tainability. The article presents the general theoretical foundations of the concept of social sustainabil-ity, the main milestones of its formation, key principles and concepts. The information base for the study of migration flows and their expected impact on the qualitative and quantitative characteristics of the labor potential was based on statistical data from Ukraine and countries that have provided asylum to forced migrants. The results of sociological surveys were also used, dozens of which were conducted by international organizations, foreign and Ukrainian think tanks during the war year. An analysis of the available estimates and reservations to them allowed us to formulate the author's assumption about the real number of refugees from Ukraine who have taken refuge in Europe, the United States, and Canada, as well as voluntarily or forcibly ended up in the Russian Federation. The socio-demographic charac-teristics of those who left indicate a significant outflow of working-age people, including a large number of highly skilled workers. A serious risk is posed by the emigration of children and young people, and their failure to return threatens the Ukrainian economy not only with a reduction in labor supply in the near future, but also with a deterioration in its quality characteristics and a loss of Ukraine's demo-graphic potential in the medium and long term. The massive out-migration of the population had a negative impact on effective demand, which is an important driver of the economy.The author proves the expediency of formulating and implementing the state policy of Ukraine aimed at counteracting these negative challenges, which is a prerequisite for ensuring social sustaina-bility. During the period of military operations and post-war recovery, state policy measures in the area of preserving and utilizing labor potential should be as effective as possible and aimed at creating a favorable environment for the return of war refugees to Ukraine and their reintegration into Ukrainian society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".