The State of the Practice of Value for Money Analysis in Comparing Public Private Partnerships to Traditional Procurements
Bibliographic record
Abstract
Literary sources regarding public—private partnerships (PPPs) often mention the importance of conducting a value for money (VfM) analysis to determine the value of pursuing a project through a PPP versus a traditional procurement; however, few sources detail how agencies actually use this analysis in practice. This article provides a state-of-the-practice review of VfM analysis using examples from Australia, Canada, Europe, Africa, and Asia, focusing particularly on the VfM model used by agencies such as Partnerships Victoria, The United Kingdom's Her Majesty Treasury Department, and Partnerships British Columbia. Despite its growing applications in PPP projects from all different sectors, VfM has faced significant criticisms from academics and practitioners. This article evaluates reviews of VfM, noting the weaknesses and strengths of the methodology. Using the information derived from the evaluation, this article provides a guided reference for public agencies looking to adopt this VfM methodology in their current PPP decision-making framework.
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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 | 0.220 | 0.416 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.033 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.030 | 0.025 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".