Formation of Public Councils under Regional Executive Authorities: Expertise or Representation?
Bibliographic record
Abstract
The article deals with the main problems associated with the formation of public councils under executive authorities. Вased on the analysis of US legislation, as well as practical experience in the formation of advisory committees, it is concluded that there are two main approaches to their formation – “professional” (inclusion of experts in the relevant field of activity) and “social” (inclusion of representatives of “interested communities”). It is shown that the predominance of one or another approach is determined by the goal facing the advisory body. A comparative analysis of the Russian regulatory framework in the sphere of forming public councils shows a much less developed approach, despite the existence of universal and specific requirements for members of public councils provided by the current federal Standard. The study of the practice of public councils formation, conducted in the framework of Monitoring the composition and activities of public councils under regional executive bodies of the Ural Federal District, led to the conclusion that a professional approach to the formation of public councils prevails. This conclusion is based on the study of the social characteristics of the leaders of public councils (since the leader is «the face of the council» and is selected according to his main goals). Not more than a quarter of the heads of public councils are employees of non-profit organizations. At the same time in the Sverdlovsk region, about a third of leaders work in the field of education and science, and another quarter are employees of government agencies and business organizations on the profile of public councils. Further study of the public councils formation at the regional level will be continued within the framework of the project «Public Councils under Regional Executive Bodies: Formation and Effectiveness». The project is implemented using a grant from the President of the Russian Federation for the development of civil society provided by the Foundation for Presidential Grants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".