Bibliographic record
Abstract
This book provides a systematic analysis of the Russian-Ukraine war, using the concept of resilient fighting power to assess the operational performance of both sides during the first year of the full-scale invasion. The Russian war in Ukraine began in 2014 and continued for eight years, before the full-scale invasion of 24 February 2022. It is not a new war, but the intensity of the warfighting revived many discussions about the conduct of inter-state warfare, which has not been seen in Europe for decades. This book does not aim to offer an exhaustive operational analysis of the war, but rather provides a preliminary systematic analysis across various domains of warfare using the concept of fighting power to assess the operational performance of both sides. First, the book discusses the conceptual component and the post-Cold War adaptations of the Soviet strategic tradition by both the Ukrainian and the Russian Armed Forces. Following that, it gives an evaluation of the various aspects of warfighting in the land, air, maritime and cyber domains. Then, the book examines the role of international allied assistance, sanctions and weapons delivery in strengthening the resilience of the Ukrainian Armed Forces. The book concludes with some comments on the role of inter-state warfare in the current strategic environment and future warfare. This book will be of much interest to students of military and strategic studies, defence studies, foreign policy, Russian studies and international relations. The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".