Cracks in the Sidewalk: Tactics and discourses driving the “smart city” development of Quayside
Bibliographic record
Abstract
Many nations have begun implementing “smart city” initiatives, however Canada is at a more nascent and therefore critical phase. In late 2017, Waterfront Toronto and Sidewalk Labs (a sister company of Google) partnered on a joint venture to create a new “smart city” development called Quayside. As Toronto and other global metropolises move towards becoming increasingly “connected”, the promises of smart cities are beginning to give way to problematic realities. This research project explored the ethical and socio-economic implications of “smart” technologies and discourses. Specifically, it questioned how issues of equity and inclusion are approached by smart city discourses, and how the narratives are being utilized in the pursuit of legitimizing smart urbanism. By examining the proposal for Quayside, the research examined a case study of an emerging smart city development, revealing four themes: 1) the spectrum of visibility, 2) the myth of neutrality, 3) the inclusive techno utopia, and 4) the rise of technocolonialism. These four themes outline the discourse and tactics Sidewalk Labs has utilized in pushing forward an agenda of smart urbanism. The findings show that smart cities have the potential to exacerbate the inequity which already exist in cities, even reaching to a new wave of technocolonization. For equity seeking groups such as people of colour and those with low income, who have historically been the target of state scrutiny, violence and colonization, living in a smart city may carry the risk of becoming more vulnerable. What happens when one doesn’t fit into the techno utopia depicted in Sidewalk’s MIDP? This project is intended for those working to craft digital governance policy within municipalities, urban planners engaging in smart urbanism projects, and non-profit organizations seeking to understand how smart cities may affect equity-seeking populations. In light of these findings, they can make a difference in fostering a more equitable society.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".