Increasing social anxiety in the context of globalization of migration processes as a problem of international relations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it