Renegotiation and Early-Termination in Public Private Partnerships
Bibliographic record
Abstract
Frequent occurrence of renegotiations and early-terminations in international contracts regarding the provision of public works and services through public private partnerships (PPPs) has raised concerns from various stakeholders in both public and private sectors. Renegotiations and early-terminations of PPP contracts may cause significant losses to the parties involved and reduce the perceived strengths and advantages of PPPs against traditional in-sourcing procurement. Through a comparative analysis of international government PPP guidelines and model contracts and multiple case studies of different types of PPP projects located worldwide, this study has identified and analyzed eight categories of risk events that often lead to renegotiation and early-termination in PPP practices, discussed the approaches to contingency management in view of such risk events, in particular the possible compensation methods respectively for the situations of renegotiation and early-termination, and established overall renegotiation and early-termination procedures. To improve PPP practices, public and private partners should build good relationships, prepare clear contract clauses, minimize opportunistic behavior and look for win-win solutions.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".