Public‐Private Partnerships in <scp>C</scp>anada: Reflections on twenty years of practice
Bibliographic record
Abstract
Abstract Across Canada, governments of all level have increasingly embraced public‐private partnerships (PPPs) as their preferred approach to deliver large‐scale public infrastructure. After twenty years of practice, this paper examines the evolving rationales, governance structures and partnership models that have been used to deliver PPPs in Canada. In particular, the analysis highlights common deal structures, types of risks and responsibilities that are transferred to the private sector, and the significant role that public funding continues to have. Outstanding issues facing Canadian PPPs relate to their high upfront costs, limitations placed on meaningful community consultations, and a procurement process that can inhibit design or architectural excellence.
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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 | 0.072 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.056 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.014 |
| 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".