The USA PPP Payment Mechanisms: A Comparison to the Canadian PPP Systems
Bibliographic record
Abstract
Over the years, PPPs in the USA have used different types of payments to compensate project developers. In the past decades, the dominant type was toll payment. Availability payments started to gain more popularity with performance-based PPP contracts. Internationally, some other payment types are also used in PPPs, such as operation and maintenance payment, safety payment, satisfaction payment, and end of term payment. A payment mechanism is a package that includes a set of payment type(s), performance measures, performance specifications, and penalties for not meeting the specifications. Since PPP payment mechanisms, other than toll payments, is new in the USA, it is not clear whether payment mechanisms as used in the USA projects would be similar to those used in the PPP international market regarding the payment types, payment structure, performance measures and specifications, penalties, and deduction schemes. This research investigated the payment mechanisms in transportation PPP projects in the USA and Canada. Comparative and content analyses of project agreements are used as research methods. The findings show that PPP projects in the USA tend to have payment mechanisms of fewer payment types, less sophisticated payment calculations, and less complex deduction schemes. Public agencies would use the outcome of this research to revisit and improve the design of payment mechanisms of their PPP projects.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".