‘It’s Our War as well’: Belarusian Fighters in Ukraine (2014-2023)
Bibliographic record
Abstract
The presence of foreign fighters in wars has been a factor in many countries. Foreign fighters from Belarus have played important roles in the war in Ukraine that started in 2014 and was expanded with the full-scale Russian invasion of February 2022. This article explores the motives of those who fought on both sides, and the backgrounds of some of the leading participants. It demonstrates how the uprising in Belarus that followed the flawed presidential elections of 2020 served as a catalyst for a growing number of Belarusians who perceived Ukraine’s cause as a means to bring about regime change in Belarus and end the long-time leadership of Aliaksandr Lukashenka. It explores the motives of the participants in the war, indicating that while some seek the experience of using modern weapons in a military conflict, others are swayed by official propaganda, and still others fight for ideological reasons, often because of a political crisis in their homeland, hoping that a victorious war in one country might catalyze change and bring new leaders to their own. The fact that Belarusians can be found on both sides of the fighting illustrates the divided viewpoints across the country toward the Russian invasion of Ukraine and Russia’s ‘Special Military Operation’. A large majority of Belarusians, however, are opposed to the national army being deployed to Ukraine and thus far the Lukashenka regime has avoided such involvement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".