‘Russian warship, go f— yourself!’ Human Factors in the Russia-Ukraine War and Ukraine Defying Expectations, February-December 2022
Bibliographic record
Abstract
Before Russia’s full-scale invasion of Ukraine in 2022, most analysts believed that Russian forces would rapidly overwhelm Ukraine’s defenses. Many policymakers were preparing for the consequences of Russia’s occupation of Ukraine rather than considering how best they could assist the latter in its moment of need. These analyses, however, focused almost exclusively on the militarily quantifiable assets and their disparity between Russia and Ukraine. In these areas, Russia possessed a decisive advantage. It should also be noted that these factors are important, and analysts should not preclude them in their analyses of the combat capabilities of armies. What these pundits and policymakers missed in their analyses, however, was the role of human factors in combat. While the Russian material elements were superior to those of Ukraine at the start of the hostilities, the human factors of the Ukrainian army and people were significantly higher than those of the Russian armed forces. This disparity between the two countries not only helped Ukraine overcome Russia’s initial efforts to defeat the country but also allowed the Ukrainian forces to launch an effective counteroffensive later that year. Factors in combat and combat effectiveness, however, are not static. Thus, while Ukraine maintained a superiority in the human dimension for much of the period in question, Russia’s narrowing of this gap and ongoing material superiority meant that the Ukrainian army was not able to translate this advantage into a decisive victory in 2022. As the war becomes increasingly one of attrition, however, human factors will play a decisive role in the conflict well into the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".