The Russian-Ukrainian Conflict and War Crimes : Challenges for Documentation and International Prosecution
Bibliographic record
Abstract
This book offers a multidisciplinary examination of the international crimes committed in the Russia-Ukraine War, and the challenges of their prosecution and documentation.As the largest international armed conflict in Europe since World War II, Russia’s war against Ukraine has provoked strong reactions and questions about the post-1945 world order, the utility of the war, and the effectiveness of international criminal justice. Throughout the chapters in this volume, scholars and legal practitioners from Canada, Germany, Poland, Ukraine, the UK, and the United States present the results of interdisciplinary research, insights from the perspective of other post-communist states, and first-hand expertise from directly working on the documentation and prosecution of these crimes. This offers a broader picture of post-Cold War relations and sheds light on the roots and nature of the war and the importance of regional approaches. The chapters also present some possible responses to the crimes committed in the conflict, with a focus on a victims-centered approach to transitional justice.This volume will be of interest to scholars and students of international criminal and humanitarian law, security studies, peace and conflict studies, and Eastern European history.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".