Dynamics of labor migration in the Republic of Bashkortostan
Why this work is in the frame
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Bibliographic record
Abstract
The article considers some indicators of the widespread social phenomenon in Russia - labor migration, which, according to the data for 2019, involves about 2.9 million Russians, or 4% of the employed population. These are internal labor migrants who temporarily work outside their regions. This type of labor migration of Russians has common features with temporary employment in the United States, Canada, and Australia (long distance commuting - LDC), fly-in/fly-out - FIFO). The empirical basis of the article consists of the statistical data (results of the labor force survey by the Federal State Statistics Service for 2011-2019) and the results of sociological research conducted in the region with a high level of shift employment - the Republic of Bashkortostan - in 2015-2019. The statistical data prove regional differences in the Russian shift employment: the majority (72%) of internal labor migrants live in a third of the regions with high and medium levels of temporary labor migration; in some regions, the level of temporary labor migration decreases. The sociological data show different involvement in shift employment depending on place of residence, gender and age, marital status and level of education. The same social-territorial and social-demographic features are evident at the national level. At the federal level, internal labor migration, as a tool for social-economic development, helps to solve the problem of labor shortage in certain areas and sectors of economy; therefore, such labor migration is supported by legal acts. At the regional level, it decreases the labor and demographic potential of the regions that provide labor migrants. To preserve the economic and demographic potential and to strengthen the competitiveness of such regions, we need to develop regional labor markets and labor mobility within regions.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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