Googling the City: In Search of the Public Interest on Toronto’s ‘Smart’ Waterfront
Bibliographic record
Abstract
Toronto’s Quayside waterfront regeneration project has become an international reference point for the burgeoning debate about the scope and limits of the digitally enabled ‘smart city’ narrative. The project signals the entry of a Google affiliate into the realm of ‘smart urbanism’ in the most dramatic fashion imaginable, by allowing them to potentially realise their long-running dream for “someone to give us a city and put us in charge.” This article aims to understand this on-going ‘smart city’ experiment through an exploration of the ways in which ‘techno-centric’ narratives and proposed ‘disruptive’ urban innovations are being contested by the city’s civic society. To do this, the article traces the origins and evolution of the partnership between Waterfront Toronto and Sidewalk Labs and identifies the key issues that have exercised local critics of the plan, including the public/private balance of power, governance, and the planning process. Despite more citizen-centric efforts, there remains a need for appropriate advocates to protect and promote the wider public interest to moderate the tensions that exist between techno-centric and citizen-centric dimensions of smart cities.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.031 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".