Value for Money and Risk in Public–Private Partnerships
Bibliographic record
Abstract
Problem, research strategy, and findings: Delivering improved public services at lower cost, also known formally as value for money (VfM), is often the main rationale for procuring large infrastructure projects through public–private partnerships (PPPs). However, it is unclear whether the ex ante assessments of PPPs account for key planning concerns, including limitations on community consultation, contractual lock-ins that curtail public flexibility to make future plans, and a political preference for PPPs that may influence the way that projects are structured and evaluated. This set of questions is examined for 28 infrastructure PPPs delivered in Ontario, Canada, and interviews with18 senior political, government, and private-sector participants in the province's PPP industry. We find that transferring of construction risks from government to the private-sector partners drives VfM results, and may overvalue the extent to which planning related risks can be transferred. Takeaway for practice: PPP contract structures should permit more transparency during the project planning process and preserve the flexibility of governments to control key planning tasks such as user fees, service coordination and facility expansion. Strategies might include: the unbundling of construction and operation phases of the PPP in all but the most unique situations, the use of competitive dialogue tendering to deepen public–private collaboration earlier in the planning process, and the inclusion of contract rebalancing terms to better share rather than transfer project risks. Research support: This research was funded through a Standard Research Grant from the Social Sciences and Humanities Research Council of Canada (Application Number: 110998).
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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 | 0.032 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".