Introduction: Handbook on Public–Private Partnerships in International Infrastructure Development
Bibliographic record
Abstract
The book is essentially case based. We asked our contributors to focus on one successful and one unsuccessful public-private partnership (PPP) in their country, drawing on cases from the Anglophone world of the United Kingdom, United States of America, Canada, and Australia; from Europe, including Finland, Norway, Netherlands, and Spain. There are cases from Asia, covering India, China, Hong Kong, the Special Administrative Region of China, and Singapore. Finally, there are cases from the Middle East and North Africa (MENA), looking at Morocco, Tunisia, Jordan, and Saudi Arabia. We were successful in gaining a wide range of comparative cases, which are briefly reviewed in this chapter, after first situating the phenomenon of PPPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".