Infrastructure public–private partnerships as drivers of innovation? Lessons from Ontario, Canada
Bibliographic record
Abstract
Public–private partnerships have been widely identified as key drivers of innovation in large public infrastructure projects such as hospitals, courthouses, bridges, highways, and transit lines. Yet to date, there is little empirical evidence documenting how much or what types of innovation are realized through the public–private partnership procurement process. Based on an examination of public–private partnership project delivery in Ontario, Canada over the past decade, this study shows that the innovations realized through the public–private partnership process tend to be a series of design, construction method, and material selection choices primarily aimed at lowering project cost and risk. Conversely, more revolutionary innovations in terms of iconic architecture or substantial rethinking of the approach to public service delivery are not typically achieved through the public–private partnership process. The paper concludes by reflecting on the meaning of innovation in the infrastructure sector, and identifies the specific public–private partnership procurement processes that incentivize cost-saving ingenuities over more transformational innovations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".