Country Positioning of Migration Flows in Ratings of Global Competitiveness
Bibliographic record
Abstract
The article shows results of marketing diagnosing of the migration flow development in 5 more or less developed countries of the world: Russia, the US, Canada, Mexico and Argentine. The authors studied and demonstrated the dependence of these countries on certain factors, which affect the level of population quality of life. Migration provides junction of mineral resources split by continents, countries and regions within countries and means of production with labour, it promotes meeting of population’s needs in jobs, housing, means of subsistence, social and professional mobility, changing social status and other characteristics of people life. By using statistic and comparative methods of research (correlative analysis, forecast, trend modeling) the authors managed to confirm or refute different hypotheses about labour migration development. They studied world ratings of countries by the level of expected life span, GDP per capita, weakness of states, unemployment, innovation development, competitiveness. By using the Russian Federation as an example the authors showed key challenges and advantages of migration flow. On the basis of the research recommendations dealing with improvement of migration climate in the Russian Federation were designed.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".