Increasing social anxiety in the context of globalization of migration processes as a problem of international relations
Bibliographic record
Abstract
Research background: Increasing inward and outward labor migration flows between Central Asian countries and Russia are very significant for both sides. Migration processes in the Central Asian region play an important role in stabilizing international relations, because their economic, political and social results are important for all the countries in the region. The Russian Federation is one of the countries which receives the most immigrants, along with the United States, Germany, France and Canada. Migrants with different ethnicities from Central Asia constitute most of the migratory flows to Russia. Purpose of the article: The authors aimed to analyze the growing social anxiety about the rising influx of migrants from Central Asian countries in Russia, as an indicator of the risk of developing damaging social processes. Methods: The authors draw their conclusions from the results of a questionnaire survey given to residents of Yekaterinburg in 2016 (N=485) and 2019 (N=476), and a comparison of comments on the internet from Russians in 2019 and 2020 about the behavior of migrants from Central Asian countries. The methods for analysis include a descriptive analysis, correlation analysis, content analysis, comparative analysis, words clustering analysis and quantitative word frequency calculation. Findings & Value added: The authors conclude that the increasing social anxiety from residents of Yekaterinburg about the rising influx of migrants from Central Asian has moved to the next stage of latent conflict, which R. Darendorff describes as the stage of “awareness of latent interests”. The obtained results are important for the regulation of processes inter-country relations in the field of migration exchanges.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".