State-of-the-practice in alternative delivery of transportation infrastructure construction projects in Canada
Bibliographic record
Abstract
Alternative project delivery methods enable the sharing of risks associated with transportation infrastructure projects between public and private sectors. Since the 1990s, agencies have increasingly sought alternatives to traditional design–bid–build methods of project delivery. This technical note summarizes the state-of-the-practice in alternative delivery of transportation infrastructure construction projects in Canada, through an analysis of a proprietary data source. Since 1992, a total of 77 projects accessed private financing to build transportation infrastructure in Canada (roads, urban rail transit, bridges, tunnels), with a total value (in 2024 CAD) of CAD 66 billion. Following a prolonged period of increased use of these methods, an apparent decline since the COVID-19 pandemic may indicate growing hesitancy in the industry. This recent evidence suggests the need to better understand project risk profiles and risk sharing arrangements. Overall, the findings offer a foundation for peer-to-peer benchmarking and knowledge transfer in an ever-evolving industry.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".