Analysing the Effectiveness of Legal Regulation of Contractual Relations during Martial Law in Ukraine: A Literature Review
Bibliographic record
Abstract
This article examines the current challenges in regulating contractual relationships during emergencies, especially in martial law. Its uniqueness lies in the interdisciplinary approach that integrates legal analysis, comparative jurisprudence, and economic factors while also considering the ongoing military situation in Ukraine. The primary objective of this article is to assess the effectiveness of Ukrainian legal frameworks in handling contractual relations amid martial law and emergencies, highlighting gaps in the current legislation. The article presents a systematic review of the literature on the legal regulation of contractual relations under martial law in Ukraine. It draws on academic publications, legal acts, and judicial practices from 2014 to 2024. The methodology encompasses analysing and comparing approaches to contract law in Ukraine and other countries facing similar conditions. This review enables the systematisation of existing knowledge and improves the legal regulation of contractual relations in Ukraine. The findings reveal that while Ukrainian law offers fundamental protection for contractual relations in emergencies, there are notable gaps, such as vague definitions of force majeure and the absence of specialised judicial procedures for dispute resolution. The comparative analysis suggests international practices could enhance Ukrainian legislation, especially incorporating insurance mechanisms and standardised force majeure clauses. The article concludes that there is a need for further refinement of legal regulations concerning contractual obligations during martial law, including more precise definitions of force majeure and developing specialised dispute resolution mechanisms. Adopting international best practices could significantly improve Ukraine’s legal framework in crises. Therefore, this study offers valuable insights for legislators, legal professionals, and businesses to bolster legal certainty and stability in contractual relations during emergencies.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".