Key Performance Indicators in Public-Private Partnerships: A State-Of-the-Practice Report
Bibliographic record
Abstract
This report provides a state-of-the-practice description of domestic and international practices for key performance indicators (KPIs) in public-private partnerships (PPPs). The report is based on a comprehensive literature review and eight case studies from Australia, British Columbia, the United Kingdom, and the United States. The concept for this report came from an implementation strategy in "Public-Private Partnerships for Highway Infrastructure: Capitalizing on International Experience," as well as "Linking Transportation Performance and Accountability" and "Construction Management Practices in Canada and Europe." The report identifies how government-developed performance measures reflecting societal goals such as congestion management or environmental impact are translated through KPIs and included in project documents for designing, constructing, operating, and maintaining transportation facilities. The report shows that it is possible to align projects with these higher goals. The findings are applicable to agencies that wish to align overarching organizational and societal performance measures through KPIs not only to PPP projects, but also to conventionally bid projects.\n
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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 | 0.154 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".