Анализ моделей государственно-частного партнерства и сфер их использования
Bibliographic record
Abstract
The different approaches to the classification of models of public-private partnership and their essential characteristics are highlighted in the article. The features of existing models of public-private partnership and spheres of an implementation of the PPP projects in the different countries are described in the article. The analysis of the current problems of the implementation of a PPP mechanism in the Ukraine is also made. The concession model of public-private partnerships is accentuated as an optimal way of realization of infrastructural problems in such countries as Great Britain, Canada, and France. The spheres of realization of projects of public-private partnerships in the Ukraine and other countries are reviewed. Water supply and drainage, housing maintenance and utilities, highway engineering and public health service are examined as the most successful spheres of an implementation of projects of the public-private partnerships. The analysis of existing problems of the development of public-private partnership mechanism was realized. Directions of improving of relations in the sphere of public-private partnership with an aim of realization infrastructural problems that facilitate structural reforms in strategic sectors are also considered in the article. The necessity of implementation of the foreign experience taking into account peculiarities of national market is proved.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".