Public–Private Partnerships for Higher Education Institutions in the United States
Bibliographic record
Abstract
Public–private Partnerships have become a common delivery method for diverse types of projects ranging from transportation and energy infrastructure to social infrastructure. Previous research has mainly focused on PPPs for infrastructure and other non-social projects. Although PPP projects for higher education institutions share some common attributes with their traditional counterparts, they also have unique aspects such as institutional culture as well as structure of ownership and management. Hence, the objectives of this research were to (1) conduct a systematic collection and analysis of PPP projects in higher education institutions in the United States; and (2) conduct a gap analysis to provide recommendations for future projects and lessons learned from past ones. A gap analysis of the published data on higher education PPPs was undertaken, identifying 45 educational PPP projects in the United States. The main areas of study were type of project (e.g., housing, commercial, mixed use, etc.), size, and investment made. Additionally, a questionnaire survey was disseminated to experts in the field to collect data on these projects and report on them. The results showed an increasing trend in project size between 1994 and 2018 with the majority being for housing developments while a smaller percentage was for commercial and utility projects. A geographical representation shows a large number of projects clustered in the Southern and Northeastern regions of the United States. Additionally, a questionnaire survey was used to identify samples of these projects and present them as a case study. The number of PPPs is expected to rise due to funding cuts and state appropriation cuts. Finally, the proposed recommendations can also be extrapolated for other social or infrastructure projects.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".