City of Surveillance? The Implications of Sidewalk Labs’ Resolution to Build a Smart City in Toronto
Bibliographic record
Abstract
ABSTRACT Google’s sister company Sidewalk Labs has proposed to build a smart city on the Eastern end of Toronto’s waterfront. This initiative is the first of its scale in North America. With the creation of a smart city come implications for the technological, political and cultural life of a city, that give Sidewalk Labs unprecedented power in the realm of urban governance. This study aims to examine whether or not Sidewalk Labs is offering a city of surveillance. Building on existing work on the influence of data, big tech and governance, as well as the cultural importance of neighborhoods, it aims to explain the possible outcomes of the decision to adopt such an initiative in a multicultural urban environment. Alongside a review of the literature on surveillance capitalism, governance and modern urban theory, discourse analysis of the recent Master Innovation and Development Plan (MIDP) released was conducted. Analysis of the material demonstrated a possible desire to control and lead, with data as the key instrument granting the tech company power of uncompetitive nature. The results indicate that there could be negative implications associated with the creation of a smart city in Toronto, but are not of unruly scale. On this basis, it is recommended that Canada update its privacy protection laws to include technological advancements of this scale, and require government involvement in the project at every stage.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".