State-of-the-Art of Value for Money Analysis: Determining the Value of Public-Private Partnerships
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent high-profile public-private partnerships (P3s) have generated significant interest in utilizing novel contracting methods to reduce costs and transfer risks associated with transportation infrastructure. Determining that a P3 will outperform a traditional approach to construction, financing or maintenance is not easy, however. Uncertain costs and risks extend far into the future. Governments in the UK, Canada and Australia use similar approaches to assessing P3 projects to determine their overall expense relative to the overall expense of traditional procurement or management. These “Value for Money” (or VfM) approaches involve developing a Public Sector Comparator which estimates total public-sector project cost, and then comparing that to the P3 cost estimate. Setting a value for risks retained and for risks transferred between the public and private sectors is the largest challenge. Governments in the three countries do through risk-assessment processes and meetings. Countries differ in their approaches to Value for Money analyses: the UK does the analyses at three levels – the program, procurement and project levels – increasing its quantitative precision with each step. In Canada, Quebec and British Columbia include VFM analyses in the larger assessment of a project’s overall business case, integrating the process and doing it only once. In Australia, guidelines direct that VfM analyses be done only after the project is defined and proposals from contractors have been submitted. In all cases, the VfM process is laborious and requires skilled analysis to ensure accuracy. The US has limited experience with P3s and almost none with VfM.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it