Mastering the Risky Business of Public-Private Partnerships in Infrastructure
Bibliographic record
Abstract
Investment in infrastructure can be a driving force of the economic recovery in the aftermath of the COVID-19 pandemic in the context of shrinking fiscal space. Public-private partnerships (PPP) bring a promise of efficiency when carefully designed and managed, to avoid creating unnecessary fiscal risks. But fiscal illusions prevent an understanding the sources of fiscal risks, which arise in all infrastructure projects, and that in PPPs present specific characteristics that need to be addressed. PPP contracts are also affected by implicit fiscal risks when they are poorly designed, particularly when a government signs a PPP contract for a project with no financial sustainability. This paper reviews the advantages and inconveniences of PPPs, discusses the fiscal illusions affecting them, identifies a diversity of fiscal risks, and presents the essentials of PPP fiscal risk management.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".