Friends in need, friends indeed? Explaining variation in military support to Ukraine
Bibliographic record
Abstract
Abstract The war in Ukraine has fostered a renewed sense of common purpose and solidarity in the West. It has also exposed deep-seated divisions regarding the provision of military support to Ukraine and the fate of the European strategic architecture. While some states have committed high levels of military support to Ukraine, others have limited their help to token military aid. This paper examines why democratic allies diverge in their foreign policy on Ukraine and Russia using an integrated framework of strategic, economic, and domestic incentives and constraints. It offers a Qualitative Comparative Analysis of 32 Western allies to uncover causal paths leading towards the provision of military support to Ukraine. The findings highlight the role of defence spending, geography, and threat perceptions during the first year of the war. Ultimately, the analysis identifies four causal paths covering 9 of the 13 greatest military aid contributors to Ukraine, as well as 14 of the 19 token aid donors. It reveals the Baltic states and Poland as the most typical military supporters, while Belgium, Romania, and Canada feature as typical token contributors.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".