Public–private partnerships for transport infrastructure: Some efficiency risks
Bibliographic record
Abstract
This paper models a public–private partnership (PPP) to construct a highway. It captures some of the key features of the Transmission Gully PPP. The winner of the tender recovers its costs (including capital costs) via an availability payment rather than toll revenue. While the availability payment eliminates demand risk, the winner of the tender faces cost risk: maintenance costs are only learned after construction is complete. The winning firm can make investments during the construction phase that reduce subsequent maintenance costs. As the Government faces transaction costs to replace the successful bidder, firms use debt strategically to pass on some of the cost risk to the Government. This distorts incentives to invest in maintenance cost reduction. Private financing therefore undermines some of the benefits from bundling construction and maintenance, which is often mentioned as an important advantage of PPPs.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".