Bibliographic record
Abstract
Problem, research strategy, and findings Public–private partnership models designed to facilitate greater collaboration have become increasingly popular. Scholarship on these partnerships has shown that they rely less on contracts and more on trust between partners, engage private partners early to allow for participation in project visioning, and prioritize shared decision making. However, there is a need to further define collaborative partnerships and distinguish them from more conventional models. In addition, research into the impacts of collaborative partnerships within planning processes is limited, and additional insights into their administrative structures, management, and internal dynamics is needed. I respond to these gaps by analyzing the collaborative co-creation public–private partnership formed to plan a smart city in the Quayside district of Toronto (Canada). Drawing on interviews (N = 35), participant observation, and document analysis, I found that those qualities of the Quayside partnership typical of collaborative partnership models reduced governmental oversight, facilitated conflicts of interest, and afforded the private partner substantial power. The challenges precipitated by the partnership structure were amplified through its application in a smart city context, where the private partner was a technology corporation with expansive resources and ambitions. Based on these findings, I argue that collaborative partnerships pose significant risks of privatizing planning processes and that these risks are heightened when asymmetries between partners are particularly stark.Takeaway for practice Planners should not allow a desire for greater collaboration to overshadow the necessity of divisions between public and private roles, because tension between the two is vital to partnership success. If seeking deeper collaboration, planners should ensure that responsibilities are clearly detailed in contracts to avoid ambiguities or conflicts of interest. This is especially important in projects where power differentials between partners are too significant to rely solely on trust instead of contracts.
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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 | 0.134 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".