Bibliographic record
Abstract
Public-private partnerships (PPPs) have a substantial history in Canada, evolving into a standard policy tool for governments to deliver large public infrastructure since the early 1990s. This chapter delves into the historical trajectory of PPPs in Canada, highlighting the primary motives and factors that fuelled their widespread adoption and promotion during this period. It then explores the reasons behind the recent decline in PPP popularity and offers insights into the future landscape of PPPs in Canada post the COVID-19 pandemic. The analysis shows that support for the PPP model began to wane as numerous large civil infrastructure PPPs encountered significant challenges, including cost overruns, delays, legal disputes, corporate bankruptcies, and tensions between the partners. The analysis underscores the challenges with the risk transfer protocols in PPPs that in some instances have left firms with major financial liabilities, and in others positioned the government assuming the role of the risk holder of last resort. Consequently, there is a renewed emphasis on exploring alternative procurement models, such as alliance contracting, that prioritize risk and reward-sharing over unilateral transfer, acknowledging the complexities associated with unforeseen events. The construction of the new Samuel De Champlain Bridge in Montreal, Canada, is the case discussed. This bridge is a key economic structure for the Montreal region (Quebec, Canada), for the transport of goods and the mobility of people between the South Shore suburbs of Montreal, the city centre and the North Shore suburbs. In this chapter, our intent is to illustrate a case study that can be qualified as a successful PPP for two main reasons. First, engineering innovations were achieved through an accelerated construction approach to building the new Samuel De Champlain Bridge. Second, sustainability concerns were considered during the PPP delivery model based on clear guidelines by the government of Canada, which greatly facilitated its execution.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".