When Alphabet Inc. Plans Toronto’s Waterfront: New Post-Political Modes of Urban Governance
Bibliographic record
Abstract
‘Smart cities’ has become a hegemonic concept in urban discourses, despite substantial criticism presented by scholarly research and activism. The aim of this research was to understand what happens when one of the big digital corporations enters the field of real estate and land use development and urban planning, how existing institutions respond to this, and how modes of urban governance are affected. Alphabet Inc.’s plans for Toronto’s waterfront provided insights into these questions. Our investigations traced a complex web of place-making practices that involved all levels of government, the general public, and networks of actors throughout the private sector. Methodologically, the discourse was reconstructed with local fieldwork, interviews with key actors, participating in tours and public meetings, and secondary sources. It was found that Alphabet Inc.’s plan to build a world-class digital city contained some lessons for urban studies and urban planning practice. First, Alphabet Inc.’s plans, which unfolded amidst initiatives to expand the knowledge economy, confirmed concerns that the trajectory of neoliberal, market-driven land use and speculation along the waterfront remains unchanged. Second, digital infrastructures are potentially a Trojan Horse. Third, it was seen that municipalities and their modes of urban planning are vulnerable to the political economic manoeuvrings of large corporate power. Fourth, Alphabet Inc. operates as a post-political package driven by a new coalition of politics, where the smart city is sold as a neutral technology. The controversies surrounding the project, however, stirred a civic discourse that might signal a return of the political.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".