State-of-the-Art of Value for Money Analysis: Determining the Value of Public-Private Partnerships
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".