Dynamics and Structure of International Labor Migration: Global Trends
Bibliographic record
Abstract
The article is devoted to the analysis of the process of international labor migration, the definition of factors affecting its scale, geographical directions and quality component. It was revealed that negative demographic trends in developed countries (birth rate decline, aging of the population) generate disproportions in their national labor markets, turning migration into the most important and, actually, the only source of labor force replenishment. In Western Europe, workers of foreign origin make up 18.4% of the total workforce, in Australia, Canada, and the United States it is about 20%. The effective functioning of some sectors of the economy in developed countries is already dependent on the labor of migrants. The trend to increase the share of foreign labor in the labor markets of these countries will grow. On the other hand, under the influence of scientific and technological progress, the needs of the labor market are being transformed, the demand for highly skilled and skilled labor is increasing. Through preferential migration regimes, countries are trying to attract foreign specialists, including potential (foreign students), thereby increasing the role of educational migration in the migration flow, its scale is growing, the flows of highly qualified specialists are intensifying, contributing to a change in the qualitative component of labor migration.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".