Омбудсмены, содействующие защите прав предпринимателей: мировой опыт и перспективы развития
Bibliographic record
Abstract
The article summarizes world experience of applying the concept of ombudsman to social relations, which involve entrepreneurs. The author analyses legal status of business ombudsmen (using the example of USA, Australia, and Russia), taxpayers’ ombudsmen (using the example of Georgia, Canada, USA, and Pakistan) and procurement ombudsmen (using the example of Canada) among public sector ombudsmen and self-regulatory industry-based ombudsmen, and organizational ombudsmen among private sector ombudsmen. The main tendencies of legal regulation of these ombudsmen status are determined. The author defines the perspectives of their development. The first one is variability of their terms of competence depending on social and political needs of the state. The second one is expanding of exercising the ombudsman conception to private relations purposed on alternative dispute resolution, and supporting such ombudsmen by state through their approval as alternative dispute resolution schemes. The third one is setting up mediation functions to ombudsmen contributing to business rights protection, and using the informal procedures of dispute resolution in their activity. The forth one is the search for additional guarantees of ombudsmen independence, including involvement of publicity into their activity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".