Analysis of Usage-Based Payments for Contractors' Compensation in PPP Projects
Bibliographic record
Abstract
Usage-based payments have been used as a common compensation method for several public-private partnership (PPP) delivery systems. The main reasons for using the usage-based payments include transferring the project demand risk to the PPP contractor, affecting/improving the demand volume for a project, and assuring that the costs associated with the future unexpected increases in demand would be the responsibility of the contractor. The share the usage payments take in a payment mechanism may vary depending on the selected PPP system, the allocation of the project demand risk, and the government objectives in the project. This article briefly reviews the structure of the usage-based payments, reasons for using them, risk allocation associated with their use, and the validity of the assumption for using them under various PPP systems. The analysis is based on the characteristics of usage-based payments experienced in a number of transportation PPP projects in British Columbia (BC), Canada. Based on the analysis, the objectives from using the usage payments could be achieved through other means in the payment mechanism of the project, e.g. through using expanded performance-based payments and using strong non-availability and non-performance payment deductions. For the performance-based PPP systems and in order to better match and achieve the objectives of the government, it is suggested that the usage payment be used as a “bonus” incentive payment rather than a main or “core” payment.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".