Going to study abroad as an extended brain drain: “Striving for” or “Escaping from”?
Bibliographic record
Abstract
In the context of the problem of the dynamics of intellectual losses in Russia, the structure of the main flows of outbound educational migration is determined on the basis of quarterly data from the FSB border service for the period from 2010 to the second quarter of 2023, broken down by goals and host countries. Aggregated statistics do not allow us to isolate the shares of different age groups or holders of different levels of formal education among those leaving the country, to determine returnable and irrevocable migration losses. However, the assessment of the stability or variability of the quantitative and qualitative aspects of outbound migration flows allowed us to test the null hypothesis of the absence of significant changes before and after the start of the special warfare. In the first quarter of 2022, the inertia of Russians’ orientation towards European education was still preserved, the blocked access to which during the pandemic created a situation of deferred demand. Germany and the United Kingdom maintained leading positions among foreign countries in terms of the number of Russians who left. Over the past year and a half, the flow to European countries has dried up, Turkey, the UAE and Kazakhstan have taken the first places, and the intensity of outflow to the leading country, measured on average per quarter, has increased 1.5 times. Accordingly, migration flows are transformed as a result of changes in the geopolitical situation after the start of special warfare, not only in quantitative terms, but also in qualitative terms (at the level of the composition of recipient countries), which indicates the dominance of non-academic motivation to leave the country.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".