Forms of compensation for damage caused as a result of international military conflict: realities and perspectives of national legislation and international law
Bibliographic record
Abstract
The article presents a scientific and practical analysis of the current regulatory and legal regulation, judicial practice in the field of compensation for property damage caused as a result of the armed aggression of the Russian Federation from February 26, 2022. In particular, options for the possible application of contributions and reparations as forms of compensation for damage caused as a result of the Russian Federation's military aggression against Ukraine are analyzed. The author critically analyzes the modern international legal mechanism for collecting compensation for damage caused as a result of military aggression, neither the public-law plane nor the private-law plane currently possess the necessary tools to achieve the above-mentioned goal. Existing mechanisms are quite complicated due to the presence of additional requirements and/or procedures. The author concludes that there is a need for a fundamental change in the approach to the essence of compensation payments and the compensation mechanism itself. Corresponding payments of the aggressor, which de facto are collected in international practice upon the fact of victory, cannot be considered adequate compensation. Needs a change in emphasis and approaches to such payments; the aggressor must pay after committing the aggression itself. The normative acts of the USA and Canada regarding the possibility of forced recovery of assets from the country of the aggressor in the presence of relevant grounds and decisions of authorized bodies and officials are built on other principles – namely, they can be applied by virtue of the act of aggression itself. Key words: military aggression of the Russian Federation, war, contributions, reparations, compensation, indemnity, assets, grounds for civil liability, property.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".