Innovative public-private partnerships in road maintenance and management: a contractor's perspective
Bibliographic record
Abstract
Partnerships between public authorities and private enterprises aiming at building and operating public works are not a new phenomenon. However, they have gained renewed attention since tensions appeared in the 90s in the public budgets of industrialized countries, which even contributed to the emergence of new partnership forms. In the road sector, innovative contracts have been promoted beyond sheer road construction, with a view to maintaining and managing urban and peri-urban road networks. In all cases, these contracts aim at an improved level of service for road users and at better value for money, by combining the competences of the private sector and of the public authorities. Indeed, for road users as well as for taxpayers, only the results count, in terms of mobility and safety, no matter what procurement method is used. These contracts are generally based on performance requirements rather than recipe based prescriptions. They differ in their financial structures as well as in country-specific legal frameworks. They show however similarities in performance measurement. This paper presents the viewpoint of a contractor based on examples from the authors' ten-year experience with maintenance of urban and peri-urban road networks in various countries : - In England, Managing Agent Contracts (MAC) with the « Highways Agency », as well as an urban PFI (Private Finance Initiative) contract; - In Canada, a rehabilitation and maintenance contract at Saint-Louis de France (Quebec), as well as CMA (Contract Maintenance Area) contracts in Alberta; - In Denmark, pavement maintenance contracts with municipalities. In all cases, these partnerships have reached the following objectives: - Fostering innovation by adopting a lifetime perspective; - Better risk management through better risk identification and sharing; - Improved efficiency through combination of competences between public and private sector and a « win/win » approach rather than the traditional « client / supplier » approach; - and finally, improved level of service, as attested by satisfaction inquiries of residents and road users, while achieving better value for money.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".