Compare Risk Allocation for Different Project Delivery Methods in Canada
Bibliographic record
Abstract
The transportation industry has been developing and maturing over time, with clients and owners requiring options in project delivery systems which increase cost certainty and reduce schedule delays. As owners look at the delivery systems, the question of risk must be answered and which party is best suited to control and price the risk. Through two separate experiences, we will examine the transfer of risk allocation decisions at a high level developed by the owner through lessons learned. Owners are looking to alternate project delivery methods to answer the call for guaranteed costs, assured timelines, and compliant quality, environmental, and safety in relation to ISO standards, value for money, and allocation of risks. This paper briefly discusses (at a high level) the allocation of risks to the owner and contractor on four predominant alternate delivery methods versus the Design-Bid-Build. They are Design-Build, Public-Private-Partnership, Construction Management at Risk, and Alliance Contracting. This paper also examines the high level differences between the various delivery models and then compares the risks associated with each during the various stages of the process. The paper will review the analysis performed through the case study on the Edmonton Light Rail Transit project 'North LRT Extension - Downtown to NAIT' by the City of Edmonton, and the risk allocation developed from lessons learned on the two initial P3's in New Brunswick, and the risk matrix developed for the 'Route 1 Gateway Project' by the New Brunswick Department of Transportation. For the covering abstract of this conference see record control number 201111RT334E.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".