Educational and Career Trajectories in Russia: Introducing a New Source and Datasets with a High Granularity
Bibliographic record
Abstract
Studying Russian society is challenging, especially during the period of the Russian military invasion. However, it takes on special significance during a period of economic and social transformation. Studying the career and educational trajectories of Russians in the context of East Studies offers a multifaceted perspective on the state of the job market and education sector and gives an understanding of the current situation of the country’s economy and social structure. The lack of data with a high level of granularity is critical, especially for studying people with a focus on their career and educational trajectories. In this article, the authors respond to this request and present two datasets that can be useful for studying spatial and temporal patterns associated with people’s life trajectories in the context of work and education. The authors utilised open data on cv s created or updated by employment portal users over the period 2015–2023 from the Federal Service for Labor and Employment (Rostrud) and prepared two cleaned datasets covering 83 regions of Russia. Dataset 1 is on the educational and career trajectories (N = 6,221,439) and Dataset 2 is on the activity of unemployed and job-seeking candidates (N = 7,662,089).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".