Urbanism under Google: lessons from Sidewalk Toronto
Bibliographic record
Abstract
Cities around the world are rapidly adopting digital technologies, data analytics, and the trappings of “smart” infrastructure. No company is more ambitious about exploring data flows and seeking to dominate networks of information than Google. In October 2017, Google affiliate Sidewalk Labs embarked on its first prototype smart city in Toronto, Canada, planning a new kind of data-driven urban environment: “the world’s first neighborhood built from the internet up.” Although the vision is for an urban district foregrounding progressive ideals of inclusivity, for the crucial first 18 months of the venture, many of the most consequential features of the project were hidden from view and unavailable for serious scrutiny. The players defied public accountability on questions about data collection and surveillance, governance, privacy, competition, and procurement. Even more basic questions about the use of public space went unanswered: privatized services, land ownership, infrastructure deployment and, in all cases, the question of who is in control. What was hidden in this first stage, and what was revealed, suggest that the imagined smart city may be incompatible with democratic processes, sustained public governance, and the public interest. This article analyzes the Sidewalk project in Toronto as it took shape in its first phase, prior to the release of the Master Innovation and Development Plan, exploring three major governance challenges posed by the imagined “city of the future”: privatization, platformization, and domination. The significance of this case study applies well beyond Toronto. Google and related companies are modeling future business growth embedded in cities and using projects like the one in Toronto as test beds. What happens in Toronto is designed to be replicated. We conclude with some lessons, highlighting the precarity of civic stewardship and public accountability when cities are confronted with tantalizing visions of privatized urban innovation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".