The Uncertainty of Forced Displacement: How Language and Violence Shaped Displacement Trajectories During Russia's Invasion of Ukraine
Bibliographic record
Abstract
Launched by President Putin to ostensibly “protect” the people living in the predominantly Russian-speaking Eastern regions, Russia's invasion of Ukraine in February 2022 produced the largest population displacement in Europe since World War II. Using unique data from a rapidly deployed online survey conducted throughout Ukraine and Europe from April to July 2022 (N = 7,974), this study examines how language and exposure to violence may have influenced trajectories of forced migration shortly after Russia's invasion. By exploiting the timing of the survey, it examines how contextual and conflict-specific factors shaped the (un)certainty of migration movements and beliefs about return. Results show that exposure to conflict in the form of witnessing or being injured by a blast explosion was associated with shorter-distance moves within Ukraine. Findings suggest disparate trajectories of displacement by language identities. Although the survey was only available in Ukrainian, and did not include those who fled (or were deported) to Russia, Ukrainian respondents who reported speaking Russian as both their “native” and “home” language (25% of the sample) had the highest probability of relocating to nonbordering countries such as Germany and the United Kingdom. Independent of their origin and destination, Russian-speakers were also more likely to be in transit or uncertain about their destination, and less hopeful about a potential return. Thus, Russia's invasion created profound uncertainty for Russian-speaking Ukrainians and appears to have pushed them even farther away.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".