Suckers and Free Riders: The Determinants of Military Burden-Sharing in the Russo-Ukrainian War
Bibliographic record
Abstract
The distribution of military assistance to Ukraine shows significant free riding, but not necessarily from the usual suspects. Relative to GDP, small allies such as Estonia, Denmark, and Sweden have taken on a much greater share of the military burden in Ukraine than larger allies such as the United States, Germany, France, and the United Kingdom. Even in absolute terms, Denmark and the Netherlands have contributed more military aid than G7 countries like France, Italy, and Canada. What explains such discrepancies? This paper analyzes why some Western allies militarily support Ukraine more than others. It examines the provision of military aid to Ukraine by Western allies from January 2022 to June 2024 and assesses whether the determinants of support at the onset of war are the same as those accounting for enduring support in a protracted conflict. The paper finds that while a balanced mixture of interest-based, non-material, and domestic factors best explains the variation in military support to Ukraine during the first year of the war, non-material factors lose some significance in the second period. While geographic proximity remains a consistent factor, the significance of oil dependence, executive autonomy, and government ideology has evolved, suggesting that countries are adapting their foreign policies in response to the ongoing conflict. Considering the discrepancy between the two examined periods, the paper calls for further theory development to understand burden-sharing at later stages of the war in Ukraine and armed conflicts in general.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".