Controlling the Implementation of the Public-private Partnership (PPP) Projects in the System of Local Strategic Management
Bibliographic record
Abstract
Due to the involvement of the private capital and the management performed by the private sector, the public-private partnerships (PPP) will be able to weaken the financial constraints and to promote the efficient development of the public infrastructure as well as the provision of the public services. In order to increase the efficiency and the effectiveness of the implementation of the PPP agreements the local authorities are required to control their execution. The types and the forms of control, exercised by the akimats in the course of implementation of the PPP projects are described in this article, the stages of development of the PPP legal framework in the Republic of Kazakhstan are studied herein. The results of the study of the implementation of the investment projects are presented by the author; the factors, reducing the efficiency of use of the budgetary funds in the framework of the PPP projects, are defined. In the article, the basic directions of formation and development of the PPP projects implementation controlling system, including the establishment of the unified PPP information system, the introduction of the modern approaches to the effective planning and budgeting in the local authorities of the Republic of Kazakhstan, the conduct of the regular training programs for the experts of the control bodies in the field of PPP are defined.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".