Mapping the PPP Market in the U.S. and Canada: Participation and Interaction of Private Firms between 1990 and 2013
Bibliographic record
Abstract
Public authorities across the United States (US) and Canada have developed a growing number of public-private partnership (PPP) agreements in order to enhance their transportation infrastructure. Within this context, the PPP market has experienced the emergence of multiple private organizations involved at different levels, within a project’s lifecycle. Although the current literature on public-private arrangements recognizes these issues, the question of how private firms organize themselves in PPP projects remains largely unexplored. We address this shortcoming by developing a longitudinal network of private participation across PPP contracts in the US and Canada from 1990 to 2013. We identify the sponsors and contractors involved in the bidding phase of highway concession initiatives, describe their roles, and examine the nature of their relationships. Our research design relies on bidding data extracted from proprietary databases and publicly available information obtained from news reports and academic journals for those design-build-finance-operate-maintain (DBFOM) projects that had reached financial close by 2013. We identified the key participants, established the number of repeat relationships among them, the nature of their transactions, and their market experience. Our main findings are summarized in network maps, where we highlight the development and interactions of private actors in the PPP market. Our main contribution is the establishment of a point of departure for studying the network structures within the private sector in the context of a public-private agreement. Further research will examine how the participants combine their capabilities and resources to achieve a successful project.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".