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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".