Public-Private Partnership as a Model of Public Infrastructure Development
Bibliographic record
Abstract
A public-private partnership (PPP), a long-term pooling of public and private partners’ resources, sharing risks, responsibilities, and results, is a successful tool employed in many countries to develop public infrastructure. The article argues that PPP is the optimal cooperation between the state and private sectors to solve social and economic problems. Even though PPP as the phenomenon of the modern economy appeared not too long ago, it had objective prerequisites, including the prior forms of cooperation between the state and individuals. The study of these prerequisites reveals the development patterns of PPP development, including its effectiveness, that allows politicians to harness them to develop an optimal state policy in the area. The article provides a comparative analysis of the PPP abroad to assess the intermediate results of the PPP development in Russia. In many countries (Canada, France, and Great Britain), a PPP as an economic model for developing the infrastructure complex has proved its viability and significantly contributes to social and economic development. It is essential that in addition to direct economic effects, a publicprivate partnership might result in indirect influence, including a positive impact on institutional development. Public-private partnership in Russia has not yet found wide application for improving the infrastructure complex. Therefore, it does not have enough impact on economic development. Despite the large volume of research on PPP, there are few comparative studies of PPP development in countries at different levels of socio-economic development. Relying on the conducted study of PPP in Russia and the leading foreign countries, the strategic model of PPP proposed by the author as well as broader foreign experience in PPP, the article provides recommendations aimed at better utilization of the opportunities provided by fostering of this form of cooperation between the state and individuals and increasing its role in the economic development of Russia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".