Modern digital practices of labor dispute resolution: theoretical and legal aspects and prospects of implementation in Ukraine
Bibliographic record
Abstract
The article provides a comprehensive theoretical and legal analysis of the concept, methodology, and practical aspects of implementing Online Dispute Resolution (ODR) systems within the framework of labor relations in Ukraine. The relevance of this study is driven by the urgent need to modernize traditional mechanisms for protecting labor rights amidst rapid societal digitalization, the widespread adoption of remote and hybrid work models, and the significant challenges posed by martial law, which hinder physical access to judicial institutions for many citizens. The author defines the legal nature of ODR as an innovative synthesis of alternative dispute resolution (ADR) methods, such as mediation, negotiation, and arbitration, with advanced information and communication technologies, effectively shifting the concept of justice from a physical location to a high-tech service. The article thoroughly examines the primary functional models of ODR, including e-negotiation, e-mediation, and e-arbitration. Particular emphasis is placed on the concept of «algorithmic justice» and the potential of artificial intelligence systems for the preliminary assessment of litigation prospects based on data analysis, drawing on international benchmarks like the Solution Explorer technology. By conducting a systematic review of international practices in Canada (the Civil Resolution Tribunal), the USA, and EU member states, the author highlights the critical advantages of digital procedures for parties in labor conflicts, such as cost-effectiveness, procedural speed, psychological comfort, and territorial inclusivity. These factors are essential for ensuring the protection of rights for internally displaced persons and citizens residing abroad. Furthermore, the study identifies and categorizes potential risks associated with digital implementation, including the «digital divide,» challenges in party verification, cybersecurity threats, and the risk of «algorithmic bias» in automated decision-making processes. The paper substantiates the necessity for a comprehensive legislative framework for online labor dispute resolution through strategic amendments to the Labor Code of Ukraine and the Law of Ukraine «On Mediation». The research culminates in the proposal of an original model for a national platform, «E-Labor Arbitration,» intended to be integrated into the «E-Court» ecosystem or the «Diia» portal, aimed at creating a transparent, rapid, and accessible mechanism for labor rights protection in the digital era.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| 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".