Management of Force Majeure Risks in Canadian PPP Transportation Projects
Bibliographic record
Abstract
The successful implementation of public-private partnerships (PPPs) requires the contractual obligations and rights of the public and private parties to be clearly defined. The occurrence of risks in PPPs may negatively impact the parties’ abilities to perform their obligations. Force majeure (FM) risks represent a risk category that requires delicate management as it may cause tremendous losses to the private party. However, little has been published on how these risks were defined, allocated, and managed in PPPs. This paper investigates these issues through a case study approach that analyzes the agreements of five PPP transportation projects in British Columbia through content analysis. The findings show that not all FM events are dealt with as FM risks; only the most severe are called eligible FM (EFM) risks. These risks provide compensation events that relieve the parties from their obligations, allow termination of contracts, and provide for compensating the balance of the private debt, equity, and labor payments. Non-EFM events provide for continuation and compensation for the expenditure above the maximum insurance coverage. If the expenditure and restoration time exceed particular thresholds, termination may occur. The analysis should assist the PPPs on how to better manage the FM risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".