Using Public-Private Partnerships to Improve Transportation Infrastructure in Canada
Bibliographic record
Abstract
There is general agreement among diverse groups and individuals that Canada’s transportation infrastructure desperately requires improvement. As governments move to confront this challenge, it is not enough that they simply commit to building more roads or bridges; the infrastructure must be built on time and on budget, be of high quality, and be well-maintained.The conventional way for providing transportation infrastructure involves the government hiring a firm to build the facility based on a prescriptive design. The government then takes responsibility for operating and maintaining the facility and perhaps outsources some aspects of care to private companies. With a history of construction-cost overruns and time delays as well as other notable problems, the conventional process has not served Canadians well.Public Private Partnerships (P3s or PPPs) are an alternative to the conventional process. P3s capture benefits of the marketplace while achieving the government’s goals for public infrastructure. This report examines the potential improvements P3s can bring to Canada’s transportation infrastructure. At the outset, it is important to note that, while P3s offer several advantages over the usual process, they may not be well suited for every transportation project. Put plainly, P3s are an important option in the government’s tool kit and should be given consideration when appropriate.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".