The Contribution of Migration to Sustainable Development: Western Vector of Migration from Central Asia (the US Case)
Bibliographic record
Abstract
The 2030 Agenda for Sustainable Development recognizes for the first time the favorable contribution of migration to sustainable development. This document is called the “declaration of interdependence”, which is especially true for migration, which connects countries of origin and destination countries and has a huge impact on the lives of millions of migrants and their families. In the context of globalization, international migration has become a key challenge for both global development as well as for some state actors. At the present time there are several attractive centers on the planet for migration, which include highly advanced European Union countries, the United States, Canada and others. Although a number of issues of the migration process from the Central Asian republics to the United States are considered to be typical for any nation in the context of globalization, there are some regional peculiarities. The issue of immigration of the Central Asians to the United States is undoubtedly relatively a new and less studied phenomenon. The article provides a comparative analysis of the causes and consequences of the migration movement “overseas” in a country context, and its impact on diaspora relations. The goal of the article is to analyze of the migration process from the Central Asian republics to the United States in connection with international migration trends in the context of the Sustainable Development Goals (SDGs), and the Global Compact for Safe, Orderly and Regular Migration (GCM).
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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 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".