Aiding Ukraine in the Russian war: unity or new dividing line among Europeans?
Bibliographic record
Abstract
Abstract The Russian invasion of Ukraine has caused a seemingly high level of unity amongst Europeans in support of Ukraine. However, this article uncovers some inter- and intra-country fault-lines in public opinion across and within 16 EU countries and the UK regarding pro-Ukraine aid initiatives by using a two-wave design with data from the EUI-YouGov survey conducted in April and September 2022. Findings show that support is relatively stable but varies a lot depending on the specific measure and between countries. We uncover lowest support for measures that go against the self-interest of Europeans such as deploying troops and accepting higher energy costs. Frontrunners of Ukraine support are geographically close to Russia and located in both Western and Eastern Europe (though not exclusively), whereas laggards are countries of Eastern and Southern Europe with a history of Russian ties during the Cold War. Yet within countries, Ukraine support does not follow a simple pre-determined ideological pattern of the left and right. Most countries with lower overall support for Ukraine display a higher level of polarization between supporters of the incumbent versus the opposition party. Understanding these fault-lines is important for insights on current and future levels of Ukraine aid across Europe.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".