Value for Money and Risk Relationships in Public–Private Partnerships: Evaluating Program‐based Evidence
Bibliographic record
Abstract
Abstract Value for money – VfM (the provision of improved public infrastructure and services at lower cost) – is a central rationale for the deployment of public–private partnerships (P3s). However, it remains unclear how VfM is actually created in P3s. There are several issues that surround theex anteevaluation conducted during P3 assessment, including: transparency of the process, engagement of stakeholders, potential restrictions on current and future public sector flexibility, and political influences that call into question the legitimacy of the process. This study examines these issues using Alberta's P3 projects executed since 2003, and interviews 35 key participants and stakeholders. The findings suggest that while the transfer of risk from the public to the private sector is a key driver of VfM, it may overstate the extent to which planning related risks can be transferred. This paper recommends enhanced VfM component disclosures and transparency as the evaluation process evolves. Furthermore, a more rigorous approach to risk conceptualisation and valuation should be adopted. Risk allocation should be about managing not only occurrence, but also impact of the risk factor. Finally, political interference must be moderated to allow for the optimal realisation of the best possible choices presented by P3 deployments.
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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 | 0.232 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".