Legal Guarantees for the Protection of the Labor Rights of Persons with Disabilities in Ukraine
Bibliographic record
Abstract
The content of universal and European regional international instruments on protection of labor rights of persons with disabilities is analyzed in the paper. The conclusion is made that Ukraine should take into account the positive foreign experience of the leading countries in the field of state policy of support for persons with disabilities and its implementation. It is justified that the world experience in the field of employment for persons with disabilities will facilitate the practical implementation not only of government programs but also of measures to protect the interests of persons with disabilities. A number of scholarly publications on the subject have been investigated, containing suggestions by scientists to improve promising labor law regarding the manifestations of discrimination on the basis of disability in the field of work. Some theoretically sound recommendations for improving the legal regulation of the protection of the labor rights of persons with disabilities and ensuring that they fulfill their responsibilities were developed. General conclusions have been drawn that the problem of the legal mechanism for the protection of the labor rights of persons with disabilities needs reforming and improvement. Some proposals for improving the legal regulation in this field were given.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".