Developing the Operational City: Sidewalk Labs & Toronto’s Eastern Waterfront
Bibliographic record
Abstract
In social media and the popular press, there is much discussion over the City of Toronto’s decision to partner with Google on their Eastern Waterfront development, however, there has not been enough scholarly research on its long-term implications. First, this public-private partnership signals a new model for urban design professionals. Second, intelligent infrastructure will be harvesting citizen data continuously and autonomously twenty-four hours per day. Google will build on its reputation as the world’s largest search and data aggregation company by layering the city with a ubiquitous wireless network on top of city services, forming an informational stack that will invisibly orchestrate communication, economics, and energy. Artificial intelligence software will analyze the resultant mass of citizen data, and use it to automatically inform decisions that will shape future city services. Those analytical feedback loops will create an operational city, one where cars drive themselves and smartphones know what residents want and where to find it – all in real time. Is this the future vision for our 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".