Bibliographic record
Abstract
Smart cities are a growing area of scholarship, but they are understudied in the literature in urban planning. This case study of the Sidewalk Toronto/Quayside project offers insights into the smart city planning process and its actors. The public-private partnership between a tripartite government agency, Waterfront Toronto, and an Alphabet Inc. subsidiary, Sidewalk Labs, to plan one of the most significant urban smart city projects in North America, and its cancellation after two and a half years, provides an opportunity to study a smart city project that has not been undertaken anywhere else at this scale. My research examines the Sidewalk Toronto/Quayside smart city planning process through semi-structured interviews, participant observation, and discourse analysis. This dissertation is organized into a three-paper model, informed by three objectives: to contextualize the Quayside project within the history of waterfront regeneration in Toronto, to understand the origins and motivations underpinning the “co-creation” partnership between Waterfront Toronto and Sidewalk Labs, and to trace how the partnership shaped public engagement in the planning process. This research expands smart city planning literature by further illuminating the unique conditions and considerations of smart cities within established planning processes, including waterfront regeneration, partnership development, and participatory planning. Based on my research findings, I make three central arguments. The first is that Quayside represents both a repackaging of established entrepreneurial discourses on Toronto’s waterfront and a new phase of Toronto’s waterfront development in that the project was specifically designed to ensure the organizational longevity of Waterfront Toronto rather than to meet development objectives that were previously prioritized in the corporation’s work. Second, I argue that smart city planning processes are a fraught application for collaborative public-private partnership models given that these models depend on equity and mutual respect between partners, which is difficult to achieve in smart city partnerships where significant resource and knowledge asymmetries exist between large technology corporations and government agencies. Third, I argue that regardless of the scale and volume of public engagement opportunities, a smart city planning process cannot be citizen-centric if participation is directed and facilitated by a private smart city actor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".