Breaking the stalemate: Europeans' preferences to expand, cut, or sustain support to Ukraine
Bibliographic record
Abstract
The Russian invasion of Ukraine in February 2022 marked a turning point for European security. Public support is crucial for sustaining the significant aid European countries have provided to Ukraine. In this article, we focus on two key aspects of public opinion on the war in Ukraine: whether Europeans want to increase, decrease, or maintain current support, and what drives these attitudes. Using survey data from six European countries fielded in June 2024, we find little evidence of war fatigue among the European public. Most respondents express satisfaction with current aid levels, and a narrow majority in most countries even supports increasing aid, while around 10 percent firmly opposes any support. Interestingly, preferences are unrelated to whether a country has been a large or small donor. Furthermore, preferences are shaped by economic evaluations and national identities. Citizens who negatively assess the domestic economy are less supportive of aid, while personal financial concerns have no impact. In addition, citizens with strong feelings of national identity are also less supportive of aiding Ukraine. We discuss the implications of these findings in light of the ongoing war in Ukraine and the challenges they pose for sustaining public support crucial to European security.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".