Achieving Agency Goals in Public-Private Partnerships through Key Performance Indicators: Application of Existing Contract and Specification Theory
Bibliographic record
Abstract
The U.S. infrastructure system is deteriorating at a rate that is outpacing its available public financing, an obvious debilitating combination. One solution to “tip the scales” and ease agencies’ financial burdens is the use of public-private partnerships (P3) for public infrastructure projects. Performance management is an important tool to help P3s deliver value for money. It relies on key performance indicators (KPIs) to indicate progress toward achieving outcomes. Developing KPIs is challenging as they must remain valid and pertinent throughout the term of a P3 program, which can range from 25 to 50 years and beyond. Existing literature on KPIs focuses on general indicators of successful projects, the most important KPIs for differing project stakeholders, and best practices. What the literature lacks is how to incorporate KPIs into contracts that are quantifiable, enforceable, and realistic in their execution while dynamic enough to be effective over time. This paper uses a combination of flexibility in legal contract theory and international agency guidelines for performance specification writing to present guidelines that will assist agencies and inform researchers on formulating KPI contract language to reach agency goals throughout the duration of the project.
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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 | 0.080 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".