A “Ukrainian Campaign” for the Russian Armed Forces? Logistical Deficiencies During the Invasion of Ukraine 2022
Bibliographic record
Abstract
The attack on Ukraine by Russia on February 24, 2022, stunned the international public opinion, even though a series of warning signs had been pointing to this attack for several years. A large number of papers were published to comment on the geopolitical stakes of a war in the heart of Europe, and its implications on the world economic order. On the other hand, little attention has been paid to the logistical deficiencies that have manifested themselves at the level of the Russian armed forces, whose strength and recurrence explain in part the renunciation of the Russian general staff to take control of Kiev. While major works have been conducted on military logistics to underline the importance of supplies in the success of a strategy of territorial conquest, the conflict between Ukraine and Russia indicates that failures at this level can undermine this strategy. The article raises the question of the key role of logistics as a support for military action, its originality being to highlight the failures rather than the successes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".