Bibliographic record
Abstract
In August 2015, Google reorganized its various interests as a conglomerate called Alphabet Inc. Under the new umbrella, Google’s search, data aggregation, and advertising subsidiaries, were joined by Sidewalk Lab and its suite of urban products: high-speed broadband services, Android Pixel2 phone, mobile mapping, autonomous cars, artificial intelligence, smart homes, and all the data captured therein. The City of Toronto’s recent award to Alphabet’s Sidewalk Lab for design services has sparked a heated controversy among urban planners and citizens alike. Toronto’s decision not only signals a different model of professional practice, but it also represents a conceptual shift away from citizen to urban consumer. By engaging a private technology company, one that passively captures data on its customers and then re-sales that data to third parties, Toronto’s smart city points to a significant change in the understanding and practice of contemporary urban planning and design. Acknowledging the city as a site of disciplinary disruption, this paper introduces Bratton’s stack theory as a way to understand networked urbanism more generally, and Waterfront Toronto specifically. We build on Bratton’s position by closely examining twenty-first century histories and anthropologies related to the Internet, privacy, and the dominance of big data. Our principal concern is with the transformation of personal and environmental data into an economic resource. Seen through that particular lens, we argue that Toronto’s smart city has internalized relations of colonization, whereby the economic objectives of a multinational technology company take on new configurations at a local level of human (and non-human) information extraction – thereby restructuring not only public land, but also everyday life into a zone of unmitigated consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".