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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".