Toronto’s failed smart city: intellectual property, data, and bad governance
Bibliographic record
Abstract
Public debates over smart cities typically focus on questions of surveillance, privacy, and possible socio-economic benefits from data-driven technologies. This chapter argues that public officials must have a critical understanding of intellectual property (IP) to effectively create and operate smart cities, complemented by a sound knowledge of data governance strategies, including how – or if – data should be commodified. When city officials lack this critical knowledge, the chapter argues, the result is ineffective policy, specifically badly designed smart cities with unevenly distributed innovation, ineffective public services, and smart-city vendors capturing the lion’s share of the revenue stream from smart-city technologies. The chapter examines Sidewalk Labs’ plans between 2017 and 2020 for a smart city project in Toronto. Drawing from critical data studies and the International Political Economy literature, the chapter examines primary documents related to the project, specifically the Google company, Sidewalk Labs’ June 2019 four-volume 1,500-page project plan.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".