An Overview of Public Private Partnership
Bibliographic record
Abstract
In many developed and developing countries there has been a move toward an increased reliance on Public Private Partnerships (PPPs) for infrastructure development. This involves an engagement with, or participation of, private companies and the public sector in the financing and provision of infrastructure. In most countries these PPP arrangements have been aimed at overcoming broad public sector constraints in relation to either a lack of public capital; and/or a lack of public sector capacity, resources and specialized expertise to develop, manage, and operate infrastructure assets. In a number of countries Public Private Partnerships are now commonly used to accelerate economic growth, development and infrastructure delivery and to achieve quality service delivery and good governance. The spectrum of nature and types of public private partnerships (PPPs) are vast, making a precise and complete definition of a PPP difficult. However, significant developments in the use of PPP in many countries have made it increasingly important to understand these practices, as well as to unveil any underlying common principles and problems and to capture and develop a body of good practices, where such can be achieved.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".