Smart Cities in Canada: Digital Dreams, Corporate Designs
Bibliographic record
Abstract
Experts from across the country investigate the "smart city" trend in urban planning as it is showing up in different Canadian municipalities. "Smart cities" use surveillance, big data processing and interactive technologies to reshape urban life. Transit riders can see the bus coming on a map on their phones. Cities can measure and analyze the garbage collected from every household. Businesses can track individuals' movements and precisely target advertisements. Google's failed Sidewalk Labs proposal in Toronto, which drew sharp criticism over surveillance and privacy concerns, is just one of the many smart city projects which have been proposed or are underway in Canada. Iqaluit, Edmonton, Guelph, Montreal, Toronto and other cities and towns are all grappling with how to use these technologies. Some cities have quickly partnered with digital giants like Uber, Bell and IBM. Others have kept their distance. Big tech companies are hard at work recruiting customers and shaping – sometimes making – public policy on data collection and privacy. Smart Cities for Canada: Promise and Perils is the first book on smart cities in Canada. In this collection, experts from across the country investigate what this new approach means for the problems cities face, and expose the larger issues about urban planning and democracy raised by smart city technology. This is a valuable, timely, independent‐minded book for Canadians. [From Smart Cities in Canada: Digital Dreams, Corporate Designs - Lorimer Adult ]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".