Information Openness of Regional Development Agencies in Russia: Trends and Forecasts
Bibliographic record
Abstract
During last decades Russia was in the process of forming a market of regional development institutions, the structure of which includes such managing entities as regional development corporations (agencies). The article examines the information openness of Russian regional development corporations (RDC). It gives quantitative assessment and shows the qualitative transformation of this phenomenon in 2016 and 2020. The official websites and portals of these organizations are used as information base. Comparison information openness ratings of the Russian RDC for 2016 and 2020, built by the authors, allowed establishing few important facts and trends’ development. Firstly, the number of RDCs is slowly but surely growing. Secondly, their information openness has slightly increased over the last four years. Thirdly, the difference between the indicators of information openness of the RDC has sharply decreased, what indicates an increase in competition between these structures in the all-Russian information market. Fourthly, the work to improve awareness of RDC activities is spontaneous and does not involve any reporting standards. The experience of Canada and Australia was considered to identify management reserves in the work of Russian RDCs. That allowed to formulate few proposals. First, it is advisable to increase the number of domestic RDCs by 2–3 times. Secondly, a unified standard for RDC corporate reporting and a Federal portal with their contact details are necessary. Thirdly, RDC should not only participate in the implementation of regional projects, but also develop a promising model for the development of the territory, considering its specifics, which is currently absent.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".