Social dialogue instead of conflict: foreign experience of effective labor relations regulation
Bibliographic record
Abstract
The article examines modern approaches to the development of social dialogue as a key instrument for ensuring a balance of interests between the state, employers, and employees in the context of labor market transformation. The international experience of Canada and Spain is analyzed, demonstrating different models of applying social dialogue to regulate labor relations and address socio-economic challenges. It has been established that the Canadian project Equi’Vision ensured the practical implementation of the principle of equal pay for work of equal value, contributed to enhancing transparency in remuneration, and helped identify discriminatory factors in the labor market. It is emphasized that this project laid the foundation for introducing a system of monitoring gender equality in the field of labor. It has been proven that active cooperation among the state, trade unions, and employers within the framework of social dialogue can effectively promote the elimination of inequality and strengthen trust among social partners. It has been found that the Spanish experience, represented by the so-called “Riders’ Law,” confirmed the capacity of social dialogue to influence the formation of national labor legislation, taking into account the challenges of the digital economy. As a result of tripartite negotiations, the employment dependence of platform delivery couriers was recognized, providing them with access to social protections and collective bargaining rights. It has been demonstrated that social dialogue is capable not only of responding to change but also of shaping institutional conditions for the adaptation of the labor market to digital transformations. It has been substantiated that for Ukraine, under martial law, structural economic transformation, and labor market digitalization, these models have practical significance. It is proposed to apply the Canadian experience to enhance transparency in remuneration and implement analytical monitoring, and the Spanish experience to adapt legislation to new forms of employment. It has been concluded that combining these approaches will contribute to the formation of a modern system of social dialogue in Ukraine, oriented toward mutual responsibility and sustainable socio-economic development.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.029 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".