Russia's Long-Distance Commuters in the Oil and Gas Industry: Social Mobility and Current Developments - an Ethnographic Perspective from the Republic of Bashkortostan
Bibliographic record
Abstract
Long-distance commuting (LDC) to the oil and gas fields in the Russian north has become a visible social development in today's Russia. This sector provides people in rural communities with employment, high salaries, and the opportunity to increase their social mobility. In the Republic of Bashkortostan, both oil extraction, as well as LDC to Western Siberia, have a long tradition. A recent development, however, is that increasing numbers of people from rural villages without such ties to oil extraction are now entering the oil and gas sector. This article provides examples of why these people are commuting and what is behind this turn of events. Besides economics, both local developments as well as developments within the oil and gas sector (the most thriving industrial sector in Russia) are contributing factors. People from rural regions see employment in this wealthy sector as a chance to better themselves. Even though some working conditions have been condemned as inhumane, the sector is able to provide a wide variety of employment possibilities. The oil and gas sector is seen as the most stable and most profitable sector in Russia from a local perspective. Keywords: LDC, oil and gas industry, rural Russia, Republic of Bashkortostan, social mobility
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".