Availability Payments and Key Performance Indicators
Bibliographic record
Abstract
Recent economic problems have led to a potential increase in the adoption of an availability payments approach by U.S. transportation agencies when creating public-private partnerships (P3s). In doing so, the public sector assumes 100% of the demand risk, relying much more heavily on performance management systems that contain key performance indicators (KPIs) to ensure optimal private partner performance during operations and maintenance. Many existing performance management systems, however, suffer from a lack of alignment among KPIs chosen to ensure that all P3s meet societal or agency goals as well as project goals. Furthermore, the choice of KPIs and accompanying standards has resulted in standards that are too easily met or inappropriately weighed. Examples are provided from P3 contracts for projects in Australia and Canada.
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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 | 0.028 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".