PUBLIC-PRIVATE PARTNERSHIPS IN THE U.S. AND CANADA: CASE STUDIES AND LESSONS 1
Bibliographic record
Abstract
ABSTRACT. Governments in many industrialized nations have made concerted efforts to reduce their direct expenditures. Public-private partnerships (P3s) are one emerging method of doing so. Despite their increased use, little independent research has been conducted on the effectiveness of P3s. This article reviews recent P3 experience in the U.S. and Canada. It briefly reviews the rationale for P3s and identifies a number of major P3s in the U.S. and Canada. Evidence from six project case studies and an analysis of U.S. prison P3s suggests that the private sector often attempts to gain as much as it can at the expense of the public sector. Contractual costs have been high, especially in the presence of complexity/uncertainty, asset specificity, and lack of contract management skills. There has sometimes been a political imperative to prevent projects from terminating. In such circumstances, there have been instances where private or public partners have behaved opportunistically. Unless public sector managers recognize that they must design contracts that both compensate private sector partners for risk and then ensure that they actually bear it, P3s will not improve allocative efficiency (make society better off). This article’s purpose is to draw attention to the importance of appropriate institutional design.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".