The potential role of public–private partnerships in the upgrade of port infrastructure: normative and positive considerations
Bibliographic record
Abstract
There is a broad consensus on the need for the major expansion of many ports. Traditionally, ports and related facilities have involved significant levels of direct or indirect government ownership or some degree of government financing. Most governments, however, are reluctant to either borrow money to fund the needed additional capital infrastructure or to fund it directly. Public–private partnerships (P3s) are thus an attractive potential option. But are they the answer? This article examines the normative rationales for P3s and presents a positive theory perspective that focuses on the conflicting goals of public and private partners. It argues that the major government impetus for P3s is likely to be for physical port infrastructure with moderate levels of market failure, such as small to medium sized ports, and not for intangible port activities. Furthermore, small to medium sized port P3s are likely to be successful in terms of having relatively low transaction costs and lower total social costs than alternative provision mechanisms. Nonetheless, even in this situation, the different goals of public and private partners may give rise to conflict. Drawing on the global empirical evidence on P3s, this article proposes some institutional design features that will help to ensure P3 success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".